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by Jeff Nielson, Bullion Bulls:
For the past 6 1/2 months I have been warning readers that the banking Crime Syndicate (and all their political and central bank puppets) have been manufacturing a Fake Rally in the precious metals sector. They have been marching prices higher themselves, just so they can "crash" these markets much, much harder — when the Next Crash arrives.
Warning: precious metals being set up for CRASH
I have presented evidence of this Fake Rally in numerous forms over this period of time. However, this new evidence is (in some ways) the most-persuasive yet.
What does the banking Crime Syndicate do when it plans the premeditated take-down of a particular company or sector? It BUYS INTO its targeted Victim. Why? Because you can hammer a market much, much harder through the combination of shorting and long liquidation than you can by shorting alone.
Here again, I'm not engaging in speculation/theorizing, I have empirical proof. What did we see during the Crash of '08? We saw the Top 0.1% (i.e. the oligarchs) pounding U.S. markets with four solid months of long-liquidation.
Proof That the Top 0.1% Create Crashes
While the Big Banks (their tools) were hammering the same markets with relentless shorting, the oligarchs were accelerating and exacerbating the Crash with vast amounts of long-liquidation.
Why do we see Western central banks buying the shares of MAJOR gold and silver miners? There is no possible LEGITIMATE explanation or purpose for acquiring these shares. It does not advance any of the "official policies" (i.e. lies) of these fraud-factories.
There is only one explanation why Western central banks are buying the shares of gold and silver miners, at the request of their Masters. They are doing so in order that they can DUMP large quantities of these shares onto the market, along with the rapacious shorting/naked shorting of the Big Banks.
Tag-team crime. We see it again and again with this Crime Syndicate. It is the hallmark of Bully behavior. Never engage in "a fair fight". Always blind-side your Victim with overwhelming force.
Read More @ BullionBullsCanada.comHelp us spread the ANTIDOTE to corporate propaganda.
Please follow SGT Report on Twitter & help share the message.


text: Judy is one of the most loved members of the Tanooki board. In the old days, veterans of the board were called "Down+B"-- members who came from Gamers.com before 1up.com existed. Judy is one of the infamous Down+B. He is known for being a very kind-hearted individual who is nice to even the newest members of the community.

subset: commoncrawl


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Cisco Audit tool !!
- Gold, 750 points or more
Hi Netpros,
A while ago a colleague mentioned to me about a Cisco tool available for Cisco partners which could be used for auditing Cisco Devices. The tool apparently would generate a report including whether the devices will be reaching end-of-sale end-of-support. I just can't remember what that tools is called nor the link where it can be downloaded. As always .. your help is much appreciated.
yes, there is the 'Discovery Tool' (or 'Network Analysis Tool') available for Cisco Partners which you can use after a special online training; you should find more information in the PEC part or the cisco web site. After login, search for 'Discovery Assesment' in the 'Search Learning' field at the top of the page.


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Home > Product Categories > Pro-Audio > Computer Audio > USB Audio Interface > 8PRE USB
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The one rack-space 8pre 16x12 USB audio interface turns a laptop or desktop Mac or PC into a 24-bit 96kHz recording studio with eight mic inputs, 8-channel ADAT optical digital I/O and MIDI I/O. When not connected to a computer, the 8pre can function as an 8-channel analog-to-digital converter, making it two products in one. The 8pre front panel provides a five-segment level meter, 48V phantom power button, 20 dB pad button and trim knob for all 8 mic inputs. Two banks of ADAT optical provide 8 channels digital I/O, even when recording in high definition at 88.2 or 96 kHz. The TRS main outs can be connected directly to powered studio monitors with front panel volume control. Connect the 8pre via ADAT optical to another optical-equipped interface or mixer to add 8 mic inputs, seamlessly integrated into the other device's mixing environment. If you already own an optical-equipped MOTU audio interface, the 8pre is the perfect way to add 8 mic inputs directly to your system.
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Tai Lopez Restructures Success Formula with 67 Steps
67 Steps is a 67 step program that helps people unlock doors of success more easily than they may think
Ontario, Canada, (September 11, 2016) – Health, wealth, love and happiness has always been the latent goal of all human souls all around the world. For centuries, wise men and sages have preached that there is no better route to achieving success in life than awakening one's inner self. Self improvement and development have always been the keys to a more successful life whether personal, professional or spiritual. The question that surrounds the millions though is 'how'? Making things very easy for all enthusiastic individuals who wish to live a better life, Tai Lopez has introduced his 67 Steps that assure unlocking the doors to success that were otherwise invisible to the un-awakened eyes.
The 67 Step program is basically as simple as it sounds. It is a video course where Tai Lopez introduces 67 most important lessons that he has learnt in his life and sums it up for people who could use these invaluable experiences to shape their own life. This course can be life-changing for everyone who is still in the dark about how to define 'good-life' and work towards the same. Answers to how much is too much and what amounts to too little, who to turn to for seeking advice and who to share your precious time with and many more and all explained ecstatically comprehensively.
As has been expressed by innumerable fans in the 67 Steps program review, the wisdom shared in each individual step is not merely an agglomeration of ideas collected from the infinite books that Tai Lopez had been engrossed with ever since he was 16 years of age. They are instead, a living example of the direct experiences of Lopez which he gathered as he traveled around the world including spending months serving in the leper colony of India. The 67 Steps program is a living example of what perseverance, patience and determination can do to an average college dropout. By all means, it is a must watch for all who strive to live a better life.
About 67 Steps
67 Steps is a program for self development and realization that is aimed at opening doors of success for individuals in easy and effective ways. The steps are based on the personal experiences of Tai Lopez who has spent years gathering these pearls of wisdom and putting them into one comprehensive program.
For more information, please visit http://tailopez67stepsreview.strikingly.com/
Company Name: Emerald Business Board
Owner name: Zach Radford
Address: 1130 Craig Lane, K7M 7W1, Kingston, Ontario
Phone Number: 1(613)484-0717
E-mail: firstname.lastname@example.com
67 Steps is a 67 step program that helps people unlock doors of success more easily than they may think. The program is made available online for easy download.
Sep 12, 2016


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Oldsmobile automobile enthusiasts have grown accustomed to a certain level of high-value when driving their Calais around town. Your Oldsmobile dealership isn't the only place to find genuine or original equipment quality parts to repair your Calais. Anyone who operates a Oldsmobile knows that when it comes to repairing their car, truck, or SUV only the most superior replacement parts will do. Obviously, you bought your Calais because you understood you would be getting performance and a dash of luxury both rolled up into one vehicle.
On each car and truck today, the wheel lug studs must be intact in order to keep the wheels attached to the hubs. Your Oldsmobile Calais wheel lug studs can be found sticking out of the wheel hub, and the threaded shaft pokes out past the mounting holes in your wheel. Then, the lug nuts screw onto the lug studs; firmly attaching your Oldsmobile Calais wheels to the hub. When one of these breaks, you will notice vibrations as you drive coupled with uneven tire wear.
Trust in the value of the parts you get from carpartsdiscount.com and save time and money on all your upcoming repairs. Buying new Oldsmobile parts from www.carpartsdiscount.com means you are getting the best deal on the most superior original and aftermarket replacement Calais parts on the internet. At Car Parts Discount, we have real customer support agents available with enough expertise to help you purchase the appropriate Oldsmobile Calais part for your needs. Just choose your year from the list below to find the right Oldsmobile Calais Wheel Lug Stud for your 1987, 1986, 1985 model.


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Recently we shot Jockey's March catalog in Chicago with our great friends and crew. We take you behind the scenes with a little sampling of what you'll see come March!Continue Reading
It's inevitable. At some point during the winter, we'll get hit with a snow storm that is best experienced from the comfort of the couch. That storm is hitting the east coast this weekend. Stay safe, and warm. Continue Reading
The new year is here and so are plenty of resolutions. Some stick, some don't. We're hoping ours does. Live active. That's our resolution this year. We are inspired to be active. That's it. Whether that's lifting Continue Reading
We here at Jockey pride ourselves on being innovative. We invented the men's brief in 1934. We were the first company to stitch our brand on the waistband of the underwear. We created a new category in women's Continue Reading
Now that seasonal celebrations are in full swing, you may feel a certain sense of dread. Do you have the intestinal fortitude to survive a night encased in a Spandex? We'd rather you not even think about it. Continue Reading
Many of you made October a great month for stocking up on bras – not just for you but for women in need. It was all part of the Jockey® Bra Donation event, in partnership with the H.E.M.P Legacy Foundation. For each Continue Reading
While gift-giving opportunities and occasions occur all year 'round, there's no question that the majority of gifts are exchanged in December. Sometimes we know exactly what to get the people on our gift list; Continue Reading
Temperatures are dropping, leaves are changing and frost is now visible on the ground with the daily rising of the sun. In some places, snow is already falling. Just because the weather's getting chilly Continue Reading
For an effortless look that is perfect for a girls day out, try a printed maxi dress with wedges and your favorite sunnies. Maxis are incredibly flattering, always in style and bold prints are totally on-trend. Try Continue Reading
There aren't too many things in life more perfect than a weekend brunch with your best girlfriends. We have just the look to have you looking and feeling your best from stepping out the door to the last sip of your Continue Reading


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- 29 total posts
- 29 comments
Recent comments from Sunnydazible
Hi Malissa. book direct through the same hospital they will book you into. Phuket Plastic Surgery Instuite. That wing only opened this year. Otherwise they will book you with their specialised team at either Phuket, Thailand some of... READ COMMENT
Book direct with the hospital and doctor of your choosing don't go through agencies. READ COMMENT
The breasts that turn out wonderful are probably not his work. There are so many women on this site who's breasts now look pretty horrible. I would go to Phuket to get any plastic surgery done. The surgeons are extremely experienced,... READ COMMENT
Haha yeah as if your friends didn't notice before you mentioned it. Friends are great at seeing the real you not like the others who will see your boobs coming first. Im excited for you. READ COMMENT
Agreed. Its too easy to get caught up in all the business of life and let good common sense go. I went from 58kg to 108kg when I ruptured 3 discs in my back (nursing) so I couldn't do much at all and ate a lot of processed foods as it... READ COMMENT


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"We had a fabulous 2 weeks here and loved every minute. The apartment is spacious and beautiful, the views are magnificent and it's on a tropical island in Queensland, what more could you possibly want? Sharon and Stephen had thought of everything we could have wanted and even met us at the ferry port. Dining on the balcony every day looking over the magnificent sea views sitting in a warm breeze followed by a swim in one of the four on-site pools was our idea of heaven. "
NEW PICSWaterfront Apt on Tropical Island Paradise
NEW PICSWaterfront Apt on Tropical Island Paradise
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- 2 Bedrooms
- Sleeps 4
- 2 baths
About our home
Our modern and spacious 2 bedroom, 2 bathroom apartment in Nelly Bay is the perfect base for your visit to Magnetic Island, a 20 minute fast cat ferry ride from Townsville, with its International Airport, and a few minutes walk from the Ferry Terminal once you arrive on Maggie. There is also a car ferry which takes around 45 minutes to reach the Island.
The main bedroom has a king size bed and we wake up in the morning to fabulous sea views across Nelly Bay, sipping our coffee and watching the comings and goings into the harbour. There is a large ensuite bathroom with sea views from the shower and separate bath. The large second bedroom has a queen size bed. There is a second bathroom/toilet which also has the 'European' laundry.
We have a well equipped kitchen to cook up a storm, or use the BBQ on the balcony. Dining inside or out, the choice is yours. The lounge/diner/kitchen are open plan with sliding doors opening onto the balcony. TV/DVD, cable TV, air conditioning, ceiling fans in lounge and main bedroom.
The apartment is in a gated resort and we have a choice of 4 pools to take a cooling dip in, relax in the spa or workout in the gym. There's heaps to do on Maggie....boating/jetskiing/hire a moke or scooter/fishing/hiking/snorkelling/diving/horse riding/koala spotting/9 hole golf course....or just relax and recharge the batteries!
There are four main 'villages' on the Island, Nelly Bay, Picnic Bay, Arcadia and Horseshoe Bay. We are within walking distance to Picnic Bay and Arcadia. It's easy to get around the Island by bus, grab a day pass and visit all the main areas. Alternatively hire a little topless car to buzz around in. There is a grocery store 5 minutes walk from the apartment to get your supplies.
Our ideal locations to swap are in the UK and South Africa. We can either swap (non-simultaneous is OK) or work with the points system.
- Main home
- Balcony
- Pool
- Cable TV
- Gym
- Air conditioning
- No pets
1 Review
More about us
- We are a recently retired couple
- Stephen worked in education & Sharon worked in admin
- We love sailing and have recently completed a trip up the East Coast of Aust
Dream destinations
- Anywhere
- White
- Open to offers
- Green
- Available
- Red
- Not Available
Where is it?
- Buzzy restaurants
- Stage Door Theatre Restaurant, Noodies on the Beach, Marlin Bar Tavern, Barefoot Art Food Wine
- Cool bars
- Boardwalk at Peppers Blue on Blue
- Retail therapy
- Regular markets at Horseshoe Bay and RSL in Arcadia
- Bit of culture
- Discover the heritage of the island at Magnetic Museum in Picnic Bay
- Walks in the park
- Try one or all of the eight Magnetic Island walking trails
- Spot of exercise
- On site gym, swimming, walking
- Day tripping
- boat tours and hire, jet ski tours, reef Eco tours just to name a few
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The rebellion against all there is
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Nokia Global
About Nokia
Press Releases
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White Papers
Nokia reports fourth-quarter 2004 net sales of EUR 9.1 billion, EPS EUR 0.23, and Nokia reports 2004 net sales of EUR 29.3 billion, EPS EUR 0.70
January 27, 2005
The complete press release with tables is available at:
Nokia in Q4 2004
(all comparisons are to fourth-quarter 2003 results regrouped according to 2004 organization):
- Net sales increased 3% to EUR 9 063 million (EUR 8 789 million in Q4 2003), up 8% at constant currency.
- Operating profit decreased 19% to EUR 1 357 million (EUR 1 669 million), with an operating margin of 15.0% (19.0%).
- EPS (diluted) was EUR 0.23 compared with EPS (diluted) EUR 0.25 in the fourth quarter 2003 (including a 4Q 2003 EUR 0.05 net negative impact from one-time items) on net profits of EUR 1 019 million (EUR 1 168 million).
- Nokia mobile device volumes reached a record 66.1 million units, up 19%, resulting in an estimated market share of 34%.
- Mobile Phones net sales decreased 6% to EUR 5 660 million (EUR 6 038 million), with EUR 1 060 million operating profit (EUR 1 697 million) and operating margin of 18.7% (28.1%).
- Multimedia net sales increased 29% to EUR 1 230 million (EUR 953 million).
- Enterprise Solutions net sales increased 122% to EUR 280 million (EUR 126 million).
- Networks net sales increased 12% to EUR 1 906 million (EUR 1 706 million), with EUR 260 million operating profit (EUR 41 million) and operating margin of 13.6% (2.4%).
- Operating cash flow was EUR 0.8 billion (EUR 1.3 billion) and total combined cash and other liquid assets were EUR 11.5 billion (EUR 11.3 billion) at the end of the year.
Nokia in full-year 2004
(all comparisons are to full-year 2003 results regrouped according to 2004 organization):
- Net sales decreased 1% to EUR 29 267 million (EUR 29 455 million in 2003), up 6% at constant currency.
- Operating profit decreased 14% to EUR 4 330 million (EUR 5 011 million), with an operating margin of 14.8% (17.0%).
- EPS (diluted) was EUR 0.70 (including a EUR 0.03 net positive impact from one-time items) on net profits of EUR 3 207 million, compared with EPS (diluted) of EUR 0.75 in 2003 (including a EUR 0.06 net negative impact from one-time items) on net profits of EUR 3 592 million.
- Nokia mobile device volumes reached a record 207.7 million units, resulting in an estimated market share of 32%.
- Mobile Phones net sales were EUR 18 507 million (EUR 20 951 million) down 12%, down 5% at constant currency. Operating profit was EUR 3 768 million (EUR 5 927 million) and operating margin 20.4% (28.3%).
- Multimedia net sales increased 46% to EUR 3 659 million (EUR 2 504 million), with EUR 179 million operating profit (EUR 186 million operating loss) and operating margin of 4.9% (-7.4%).
- Enterprise Solutions net sales increased 57% to EUR 830 million (EUR 529 million), with EUR 199 million operating loss (EUR 141 million operating loss) and operating margin of - 24.0% (-26.7%).
- Networks net sales increased 13% to EUR 6 367 million (EUR 5 620 million), up 21% at constant currency. Operating profit was EUR 878 million (EUR 219 million operating loss) and operating margin 13.8% (-3.9%).
- Operating cash flow was EUR 4.3 billion (EUR 5.3 billion) and total combined cash and other liquid assets were EUR 11.5 billion (EUR 11.3 billion) at the end of the year.
Nokia's Board of Directors will propose a dividend of EUR 0.33 per share for 2004 (EUR 0.30 per share for 2003).
This was a year of record-breaking mobile device volumes for our industry, which drove both the device and infrastructure markets forward. Global mobile subscriptions also rose sharply to 1.7 billion for 2004.
For Nokia, device volumes also reached new highs for the fourth quarter and full year largely backed by the ongoing boom in growth markets such as Latin America, Russia, India and China,
and brisk sales of color screen and camera phones. Our North American phone volumes were, however, disappointing.
In the fourth quarter, Nokia's record mobile device volumes, together with sequentially stable average selling prices and better-than-expected infrastructure sales and profitability, pushed our net sales and EPS ahead of guidance.
Nokia's full-year 2004 mobile device market share was 32%, based on an estimated market volume of 643 million units. I am pleased with our steady quarter-on-quarter market share gains during the second half. Based on an estimated 194 million units for the market, we achieved a 34% share in the fourth quarter largely as a result of sequential market share gains in Asia-Pacific, China and Europe/Middle East/Africa.
During the fourth quarter, we started shipping the Nokia 6630, our latest 3G smartphone, and the initial response has been positive with more than 30 3G operators including it in their offering. In Western Europe, the Nokia 6230 was the top-selling phone in the industry for the fourth quarter - a first for a camera phone. Of the 36 devices we announced in 2004, the majority had cameras and nearly all had color screens. We also introduced additional designs with ten new clamshell models, in addition to flip-open messenger devices and the Nokia 9300 smartphone for enterprises. During 2004, we continued to be the clear market leader in the GSM megapixel market and in smartphones, which combines a handheld computer with a mobile phone.
Nokia's infrastructure sales and profitability in the fourth quarter exceeded our expectations due to robust year-end spending by operators combined with our shortened delivery lead times. Profitability was also positively impacted by the successful resolution of a prior customer delivery issue as well as a product mix favoring high-margin products. In Asia-Pacific, we emerged as a clear leader in 3G WCDMA.
In our infrastructure business, 2004 was a pivotal year. We significantly expanded our presence in India and Russia, and entered new markets in the Middle East and Africa. In the second half, the pace of commercial 3G launches intensified in more developed markets, with the number of subscribers jumping to nearly 16 million by the end of 2004. By the end of the year, we were a supplier to 28 of the 63 commercially launched 3G networks.
The past year was demanding for Nokia. In response, we set five top priorities in the areas of customer relations, product offering, R&D efficiency, demand-supply management and the ability to offer end-to-end solutions. We are making good progress in these areas, and I believe we are now better positioned to meet future challenges as a result.
Our fourth-quarter performance was clearly a reflection of the ability of the Nokia team to rapidly respond to faster-than-expected market growth. I thank everyone for putting our customers first and stretching to meet and exceed our targets.
NOKIA Q4 and 2004
EUR million
Net sales
9 063
8 789
29 267
29 455
Mobile Phones
5 660
6 038
18 507
20 951
1 230
3 659
2 504
Enterprise Solutions
1 906
1 706
6 367
5 620
Operating profit
1 357
1 669
4 330
5 011
Mobile Phones
1 060
1 697
3 768
5 927
Enterprise Solutions
Common Group Expenses
Operating margin (%)
Mobile Phones (%)
Multimedia (%)
Enterprise Solutions (%)
Networks (%)
Financial income and expenses
Profit before tax and minority interests
1 463
1 731
4 709
5 345
Net profit
1 019
1 168
3 207
3 592
All reported Q4 and 2004 figures can be found in the tables on pages (10-12) and (19-24).
Fourth-quarter 2004 sales
Nokia fourth-quarter net sales increased 3% to EUR 9.1 billion, compared with fourth quarter 2003 (EUR 8.8 billion). At constant currency, group net sales would have been up 8% year on year.
In Nokia's Mobile Phones, Multimedia and Enterprise Solutions business groups, combined mobile device volume for the fourth quarter was up 29% sequentially and 19% year on year to 66.1 million units, setting a new quarterly volume record. Market volume growth for the same periods was estimated to be 22% sequentially and 29% year on year. Nokia's year-on-year volume growth was fastest in China and Latin America followed by Asia-Pacific and Europe/Middle East/Africa. This was partially offset by lower volumes in North America. Latin America, Europe/Middle East/Africa and China saw the strongest sequential volume growth for Nokia. Nokia's estimated market share for the fourth quarter was 34%, compared with 32% in the previous quarter and 37% in the fourth quarter 2003. The sequential market share gains were made in Asia-Pacific, China and Europe/Middle East/Africa.
Nokia's combined mobile device net sales in euro terms were virtually flat year on year, primarily due to significantly increased sales in China, Latin America and Asia-Pacific, and moderately increased sales in Europe, offset by significantly lower sales in North America.
Mobile Phones business group fourth-quarter net sales decreased 6% year on year to EUR 5.7 billion (EUR 6.0 billion) compared with the fourth quarter 2003. Sales were impacted by price pressure and the further weakening of the US dollar, which was only partially offset by the increase in volumes.
Multimedia business group fourth-quarter net sales were up 29% year on year to EUR 1.2 billion (EUR 953 million) with sales of imaging smartphones continuing to make a significant contribution. The sales of games devices continued to be disappointing mainly due to weak sales in the US market.
Enterprise Solutions business group fourth-quarter net sales grew 122% year on year to EUR 280 million (EUR 126 million) as a result of strong sales of business-focused mobile devices and high-end firewalls.
Networks business group fourth-quarter net sales increased 12% year on year to EUR 1.9 billion (EUR 1.7 billion) due to robust year-end spending by operators combined with our shortened delivery lead times.
Fourth-quarter 2004 profitability
Nokia Group's fourth-quarter operating profit decreased 19% to EUR 1 357 million compared with fourth quarter 2003 (EUR 1 669 million) with an operating margin of 15.0% (19.0%). Networks and Multimedia operating profit improvement was more than offset by the lower operating profit in Mobile Phones.
Fourth-quarter 2004 operating profit included a one-time positive item of EUR 50 million representing the premium return under our multi-line, multi-year insurance program, which expired during 2004. The return was due to our low claims experience during the policy period. Operating profit also included a research and development impairment in Networks of EUR 50 million related to the WCDMA radio access network project and a loss of EUR 12 million from the divestiture of Nextrom. Fourth-quarter 2003 operating profit included a negative impact of EUR 151 million related to a goodwill impairment as well as R&D impairment of EUR 108 million at Networks.
In the fourth quarter, Mobile Phones operating profit decreased 38% to EUR 1 060 million (EUR 1 697 million) with an 18.7% operating margin (28.1%) primarily due to the factors noted above under Fourth quarter 2004 sales.
Multimedia fourth-quarter operating profit increased 53% to EUR 164 million with an operating margin 13.3% due to the strong performance in imaging smartphones sales.
Enterprise Solutions fourth-quarter operating loss was EUR 43 million with an operating margin of -15.4%, in line with our expectations.
Networks fourth-quarter operating profit of EUR 260 million (EUR 41 million) and 13.6% (2.4%) operating margin was due to the successful resolution of a prior customer delivery issue as well as a product mix favoring high-margin products. Fourth-quarter operating profit in 2004 and 2003 were negatively impacted by research and development and goodwill impairments as noted above.
2004 sales
For 2004, Nokia net sales decreased 1% to EUR 29.3 billion, compared with 2003 (EUR 29.5 billion). At constant currency, group net sales would have been up 6%. Our gross margin in 2004 was 38.0%, compared with 41.5% in 2003, primarily reflecting lower sales in Mobile Phones.
In Nokia's Mobile Phones, Multimedia and Enterprise Solutions business groups, combined mobile device volume was up 16% in 2004 compared to 2003, reaching 207.7 million units, setting a new annual volume record. Market volume growth for the same period was estimated to be 31%. Nokia's volume growth in 2004 was fastest in Latin America, China and Asia Pacific, moderate in Europe/Middle East/Africa, and partially offset by volume declines in North America. Nokia's estimated market share for 2004 was 32%, based on an estimated industry volume of 643 million units, compared with 38% in 2003.
Mobile Phones business group 2004 net sales decreased 12% to EUR 18.5 billion, compared with 2003 (EUR 21.0 billion). At constant currency, Mobile Phones business group net sales would have decreased by 5%. Despite an increase in volumes, sales were negatively impacted by a decline in prices. In the second quarter of 2004, we selectively lowered the prices of certain of our products, which contributed to our stated aim of improving our market share sequentially towards the end of the year, but adversely impacted sales for the remainder of 2004. In addition, general downward price pressure in the device market and a significantly weaker US dollar negatively impacted Mobile Phones net sales during 2004 compared to 2003.
Multimedia business group 2004 net sales were up 46% to EUR 3.7 billion compared with 2003 (EUR 2.5 billion), driven primarily by robust sales of imaging smartphones, achieved with the introduction of new models reaching a broader range of the targeted customer base, offset in part by lower sales of games devices, including N-Gage.
Enterprise Solutions business group 2004 net sales grew 57% to EUR 830 million compared with 2003 (EUR 529 million) primarily as a result of increased sales of business-focused mobile devices.
Networks business group 2004 net sales increased 13% to EUR 6.4 billion compared with 2003 (EUR 5.6 billion) due to increased sales in nearly all markets as operators increased their investments in network infrastructure. At constant currency, Networks business group net sales would have been up 21%.
2004 operating expenses
In 2004, research and development expenses were EUR 3.7 billion, down 0.7% from 2003. Research and development expenses represented 12.8% of net sales in 2004, unchanged from 2003. Research and development expenses increased in Mobile Phones, Multimedia and Enterprise Solutions and decreased in Networks. R&D expenses in 2004 included impairments of EUR 115 million in Networks (EUR 470 million in 2003 in Networks).
If R&D impairments, write-offs and personnel-related restructuring costs in Networks were excluded from both the 2004 (impairments of EUR 115 million) and 2003 (personnel-related restructuring costs, impairments and write-offs totaling EUR 470 million) R&D expenses, the increase in R&D expenses would have been 10%, and R&D expenses would have represented 12.4% of Nokia net sales in 2004, compared with 11.2% of net sales in 2003.
In 2004, selling, general and administrative (SG&A) expenses were EUR 3.0 billion, down 12% from 2003. SG&A expenses were equal to 10.2% of net sales in 2004, compared with 11.4% of net sales in 2003. SG&A expenses increased in Multimedia and Enterprise Solutions and decreased in Mobile Phones and Networks.
If the return of an insurance premium of EUR 160 million and a EUR 12 million loss from the divestiture of Nextrom were excluded from the 2004 SG&A expenses, and if EUR 56 million from the sale of the remaining shares of Nokian Tyres Ltd and restructuring costs of EUR 80 million related to Networks were excluded from 2003 SG&A expenses, the decrease in SG&A expenses would have been 7% and SG&A expenses would have represented 10.7% of Nokia net sales in 2004, compared with 11.3% of net sales in 2003.
2004 profitability
Nokia Group's operating profit for 2004 decreased 14% to EUR 4 330 million compared with 2003 (EUR 5 011 million) primarily due to the lower profitability in Mobile Phones. Our operating margin was 14.8% in 2004, compared with 17.0% in 2003.
In 2004, Mobile Phones operating profit decreased 36% to EUR 3 768 million, compared with 2003 (EUR 5 927 million), with a 20.4% operating margin, down from 28.3% in 2003, primarily due to lower net sales as a result of the factors noted above.
Multimedia 2004 operating profit increased to EUR 179 million from an operating loss in 2003 of EUR 186 million, with an operating margin of 4.9%, up from - 7.4% in 2003. This was primarily due to strong sales of imaging smartphones, partially offset by a loss in our games devices.
Enterprise Solutions 2004 operating loss increased 41% to EUR 199 million compared with 2003, with an operating margin of - 24.0%, an improvement from an operating margin of -26.7% in 2003. The operating loss was in line with our expectations.
Networks 2004 operating profit increased to EUR 878 million from an operating loss of EUR 219 million in 2003, and its operating margin improved to 13.8%, up from -3.9% in 2003. This was primarily due to higher net sales, a product mix favoring high-margin products, overall profitability of 3G contracts, and a streamlined cost structure. Networks 2004 operating profit included one-time items with a negative impact of EUR 115 million, compared to one-time items during 2003 with a net negative impact of EUR 475 million, as described below.
In 2004, the Common Group expenses were EUR 296 million, down 20% from 2003.
2004 one-time items
In 2004, Nokia's operating profit included the following one-time items: positive items totaling EUR 160 million representing premium returns under our multi-line, multi-year insurance program, which expired during 2004, due to our low claims experience during the policy period; R&D impairments in Networks totaling EUR 115 million due to the discontinuation of certain products and base station horizontalization projects and an impairment related to the WCMDA radio access network project;
and a loss of EUR 12 million from the divestiture of Nextrom. Nokia's financial income in 2004 included a positive EUR 106 million item representing the gain on the sale of the France Telecom bond.
In 2003, Nokia's operating profit included the following one-time items within Nokia Networks: a positive adjustment of EUR 226 million as a result of the customer finance impairment recorded in 2002 related to Mobilcom, and R&D related costs totaling EUR 470 million, other restructuring costs of EUR 80 million, as well as a goodwill impairment of EUR 151 million related to Nokia Networks' core networks business, with a total net impact of EUR 475 million. Operating profit also included a one-time gain of EUR 56 million on the sale of the remaining shares of Nokian Tyres Ltd.
Capital structure
Operating cash flow for the year ended December 31, 2004 was EUR 4.3 billion (EUR 5.3 billion) and total combined cash and other liquid assets were EUR 11.5 billion (EUR 11.3 billion). As of December 31, 2004, net debt-to-equity ratio (gearing) was -78%.
Industry developments and outlook for 2005
Nokia continues to expect the overall mobile device market in 2005 to grow approximately 10% in volume from an estimated 643 million units in 2004 and to grow also in value, but to a lesser extent. Growth is expected to continue to be driven by replacement and upgrade sales in more developed markets with the availability of new features, services and cameras, and by new subscriber growth in developing mobile markets, as well as the widespread commercialization of 3G devices in the second half of 2005.
In infrastructure, Nokia expects the overall market in 2005 to be slightly up compared with 2004 in euro terms. We expect operators to continue building coverage and expanding capacity in growth markets as well as optimizing and expanding existing 2G networks in the more developed markets. The continued roll-out of high-speed, high-capacity 3G networks is expected to contribute to the market growth.
Nokia expects the competition in the mobile device and infrastructure markets to further intensify in 2005 due to expected slower overall growth.
Nokia outlook for Q1 2005
First-quarter Nokia group net sales are expected to be in the range of EUR 7.0 billion - EUR 7.3 billion, compared with EUR 6.6 billion in the first quarter 2004. EPS (diluted) is expected to be in the range of EUR 0.12 - EUR 0.15, including an estimated charge of EUR 60 - EUR 80 million (equal to approximately EUR 1 cent - EUR 1.5 cents per share) related to restructuring actions announced to date, compared with EPS (diluted) EUR 0.17 in the first quarter 2004.
Nokia further renewed its mobile device offering in the fourth quarter with the announcement of seven new models and the first shipments of nine models.
Of the new models announced, three were from our middle range device category. The Nokia 6020 is a business-focused camera phone in a classic Nokia design and offers Push to talk over Cellular, while the first middle range megapixel camera phone, the Nokia 3230, is based on the Series 60 Platform and features video recording and editing as well as Push to talk over Cellular. The Nokia 3128 is a fold, or clamshell, phone designed specifically for the Chinese market. All three models are expected to begin shipping in the first quarter of 2005.
Nokia also announced and has begun shipping two new models for the entry category: the Nokia 1108 with basic voice and messaging functionality, mainly targeting Asian markets, and the Nokia 2651, an affordable, fold phone targeting the Latin American market.
The company's CDMA offering was further expanded with two CDMA2000 1X camera phones, the Nokia 6235 and Nokia 6235i. Shipments of these phones are expected to begin in the second quarter of 2005.
In the fourth quarter, Nokia began shipping nine new models of which five included cameras. These models covered all product ranges and designs: two were entry models with color screens, the Nokia 2600 (GSM) and Nokia 3125 (CDMA); two were folding camera phones aimed at business users, the Nokia 6170 (GSM) and the Nokia 6255 (CDMA); and three were from the art-deco inspired Fashion Collection, the Nokia 7260, Nokia 7270 and Nokia 7280.
During the quarter, more than 30 operators worldwide, including in Japan, helped make the Nokia 6630 3G WCDMA megapixel smartphone a leader in the growing 3G WCDMA market. Nokia also began shipping the Nokia 6670 megapixel smartphone.
The Nokia 7710 wide-screen multimedia smartphone, announced in the fourth quarter, has generated significant market interest with its innovative design and rich features. The Nokia 7710, which takes pen input and recognizes individual handwriting, combines the capabilities of a smartphone and a handheld computer. This makes it suitable for a broad customer base, including consumers and business users.
In the developing area of mobile phone TV, Nokia signed several DVB-H (Digital Video Broadcast - Handheld) mobile phone TV pilots, including one with Crown Castle in the US and Bridgeway Networks in Australia, adding to the global pilots already underway. During the quarter, the DVB-H standard was finalized by the European Telecommunications Standards Institute (ETSI).
In games, sales of several key N-Gage titles started, including Pathway to Glory, Pocket Kingdom and the Colin McRae Rally. Sales of N-Gage QD started in China. By the end of the quarter, Nokia had cumulatively sold 1.3 million N-Gage devices.
During the fourth quarter, Nokia began shipping the Nokia 9500 Communicator, one of Nokia's first mobile devices on the market designed to meet corporate IT demands for customized, manageable and secure mobile access to email and enterprise software. Nokia's two high-end network security gateways, the Nokia IP2250 and Nokia IP1220, continued to gain positive traction in the market.
Nokia also introduced a strategy to become the device of choice for mobile e-mail and messaging in the enterprise market. Several Nokia alliances and activities were announced or reaffirmed during the quarter, including agreements with Good Technology, Smartner and Visto and to provide a broad range of e-mail options on Nokia business-optimized devices, such as the Nokia 9500 Communicator and Nokia 9300 enterprise smartphone.
For images of the mobile devices mentioned in the above sections, please click here:
Nokia announced eight GSM/EDGE network expansion deals, including:
- AIS in Thailand
- Comcel in Columbia
- Henan MCC in China
- O2 in the UK
- Oi in Brazil
- ONE in Austria
- Porta in Ecuador and
- Smart in the Philippines.
We also extended our strategic cooperation with Cingular in the US with a significant GSM/EDGE radio and core network contract, which also includes the possibility to support the deployment of their 3G/UMTS network.
Nokia won WCDMA 3G expansion contracts with Hutchison in Austria and Orange in Switzerland. By the end of the year, 33 Nokia customers were rolling out WCDMA networks in 22 countries.
Nokia announced Push to talk over Cellular deals with Chungwa Telecom in Taiwan, Eurotel in the Czech Republic, Oi in Brazil and with Telefonica Moviles in Mexico. Nokia also signed contracts to deliver TETRA networks in China.
In November, Nokia launched its End-user Services Consulting solution, designed to help operators and service providers develop new successful services for their consumer and enterprise customers.
Nokia outlined plans to expand the Series 60 Platform for smartphones to cover high-end and mid-range categories. As a part of this future development the core features of the Series 90 platform will merge into Series 60 during 2006.
During the quarter, Nokia launched Preminet, a solution that lets operators source leading Java(TM) and Symbian operating system software and offers them a solution for delivery, billing and revenue distribution of that content.
During the quarter, Nokia also announced important technology cooperation agreements with operators. Nokia and France Telecom plan to jointly develop new, rich media solutions to extend mobility, including remote access to content stored at home. T-Mobile and Nokia agreed to cooperate on developing T-Mobile specific applications for Series 60.
(International Accounting Standards, IAS, comparisons given to the fourth quarter 2003 results, unless otherwise indicated. Business Group fourth quarter 2003 figures are based on the unaudited, regrouped 2003 financial results as published on March 25, 2004.)
Nokia's net sales increased 3% to EUR 9 063 million (EUR 8 789 million). Sales of Mobile Phones decreased by 6% to EUR 5 660 million (EUR 6 038 million). Sales of Multimedia increased by 29% to EUR 1 230 million (EUR 953 million). Sales of Enterprise Solutions increased by 122% and totaled EUR 280 million (EUR 126 million). Sales of Networks increased by 12% to EUR 1 906 million (EUR 1 706 million).
Operating profit decreased by 19% to EUR 1 357 million (EUR 1 669 million), representing an operating margin of 15.0% (19.0%). Operating profit in Mobile Phones decreased by 38% to EUR 1 060 million (EUR 1 697 million), representing an operating margin of 18.7% (28.1%). Operating profit in Multimedia increased to EUR 164 million (EUR 107 million), representing an operating margin of 13.3% (11.2%). Enterprise Solutions reported an operating loss of EUR 43 million (operating loss of EUR 59 million). Operating profit in Networks increased to EUR 260 million (EUR 41 million), representing an operating margin of 13.6% (2.4%) and including a negative impact from R&D impairment totaling EUR 50 million. This impairment was related to the WCDMA radio network access project. Common Group expenses totaled EUR 84 million (EUR 117 million) and included a one-time positive item of EUR 50 million representing the premium return under our multi-line, multi-year insurance program, which expired during 2004. The return was due to our low claims experience during the policy period. It also included a EUR 12 million negative impact from the divestiture of Nextrom.
Financial income totaled EUR 116 million (EUR 71 million). During the quarter, Nokia sold approximately 38% of the remaining holdings in its subordinated convertible perpetual bonds issued by France Telecom. As a result, the company booked a total net gain of EUR 35 million. The bonds had been classified as available-for-sale investments and fair valued through shareholders' equity. Profit before tax and minority interests was EUR 1 463 million (EUR 1 731 million). Net profit totaled EUR 1 019 million (EUR 1 168 million). Earnings per share decreased to EUR 0.23 (basic) and to EUR 0.23 (diluted), compared with EUR 0.25 (basic) and EUR 0.25, (diluted) in the fourth quarter 2003.
Nokia repurchased through its share repurchase plan a total of 60 000 000 shares on the Helsinki Exchanges at an aggregate price of approximately EUR 0.727 billion during the period from October 15, 2004 to November 26, 2004. The price paid was based on the market price at the time of repurchase. The shares were repurchased to be used for the purposes specified in the authorization held by the Board. The aggregate par value of the shares purchased was EUR 3 600 000, representing approximately 1.3% of the share capital of the company and the total voting rights. These new holdings did not have any significant effect on the relative holdings of the other shareholders of the company nor on their voting power.
It should be noted that certain statements herein which are not historical facts, including, without limitation, those regarding: A) the timing of product and solution launches and deliveries; B) our ability to develop, implement and commercialize new products, solutions and technologies; C) expectations regarding market growth, developments and structural changes; D) expectations and targets for our results of operations; E) the outcome of pending and threatened litigation; and F) statements preceded by ''believe,'' ''expect,'' ''anticipate,'' ''foresee'',"target" or similar expressions are forward-looking statements. Because these statements involve risks and uncertainties, actual results may differ materially from the results that we currently expect. Factors that could cause these differences include, but are not limited to: 1) developments in the mobile communications industry and the broader mobility industry, including the development of the mobile software and services market, as well as industry consolidation and other structural changes; 2) timing and success of the introduction and roll out of new products and solutions; 3) demand for and market acceptance of our products and solutions; 4) the impact of changes in technology and the success of our product and solution development; 5) the intensity of competition in the mobility industry and changes in the competitive landscape; 6) our ability to control the variety of factors affecting our ability to reach our targets and give accurate forecasts; 7) pricing pressures; 8) the availability of new products and services by network operators and other market participants; 9) general economic conditions globally and in our most important markets; 10) our success in maintaining efficient manufacturing and logistics as well as the high quality of our products and solutions; 11) inventory management risks resulting from shifts in market demand; 12) our ability to source quality components without interruption and at acceptable prices; 13) our success in collaboration arrangements relating to technologies, software or new products and solutions; 14) the success, financial condition, and performance of our collaboration partners, suppliers and customers; 15) any disruption to information technology systems and networks that our operations rely on; 16) our ability to have access to the complex technology involving patents and other intellectual property rights included in our products and solutions at commercially acceptable terms and without infringing any protected intellectual property rights; 17) developments under large, multi-year contracts or in relation to major customers; 18) the management of our customer financing exposure; 19) exchange rate fluctuations, including, in particular, fluctuations between the euro, which is our reporting currency, and the US dollar, the UK pound sterling and the Japanese yen; 20) our ability to recruit, retain and develop appropriately skilled employees; 21) our ability to implement our new organizational structure; and 22) the impact of changes in government policies, laws or regulations; as well as 23) the risk factors specified on pages 12 to 21 of the company's Form 20-F for the year ended December 31, 2003 under "Item 3.D Risk Factors."
NOKIA, Helsinki - January 27, 2005
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A method for inferring medical diagnoses from patient similarities
Clinical decision support systems assist physicians in interpreting complex patient data. However, they typically operate on a per-patient basis and do not exploit the extensive latent medical knowledge in electronic health records (EHRs). The emergence of large EHR systems offers the opportunity to integrate population information actively into these tools.
Here, we assess the ability of a large corpus of electronic records to predict individual discharge diagnoses. We present a method that exploits similarities between patients along multiple dimensions to predict the eventual discharge diagnoses.
Using demographic, initial blood and electrocardiography measurements, as well as medical history of hospitalized patients from two independent hospitals, we obtained high performance in cross-validation (area under the curve >0.88) and correctly predicted at least one diagnosis among the top ten predictions for more than 84% of the patients tested. Importantly, our method provides accurate predictions (>0.86 precision in cross validation) for major disease categories, including infectious and parasitic diseases, endocrine and metabolic diseases and diseases of the circulatory systems. Our performance applies to both chronic and acute diagnoses.
Our results suggest that one can harness the wealth of population-based information embedded in electronic health records for patient-specific predictive tasks.
Over several decades, the vision of automatic systems assisting and supporting clinical decisions produced a plethora of clinical decision support systems
Recently, several methods have been released for predicting certain patient outcomes using large cohorts of patients. Two such examples are the detection of heart failure more than six months before the actual date of clinical diagnosis
In this paper, we address a different, fundamental challenge – can we leverage the corpus of EHR patient data, even with well-documented quality issues
Data description
We obtained two EHR datasets from two hospitals: (i) 9,974 patients with 15,498 admissions, admitted in several wards belonging to internal medicine (for example, cardiology, oncology) or neurology over the course of two years from the Stanford Medical Center, CA, USA (USA dataset); and (ii) 5,513 patients with 7,070 admissions in internal medicine wards at the Rabin Medical Center, Israel between May 2010 and February 2012 (660 days; ISR dataset). Each dataset includes patient demographics (gender and age), medical history (International Classification of Diseases, Clinical Modification codes (ICD-9-CM) from past in- and out-patient encounters) and hospitalization specific information including blood test results and discharge diagnoses, coded as ICD-9 codes. A subset of the patients in the USA dataset includes ECG measurements, while the ISR dataset (7,261 patients) also contains ICD codes assigned upon admission. The USA dataset includes 86 commonly administered blood tests (after filtering, see below) and the ISR dataset includes 19 blood tests. Both patient cohorts include only urgent (non-elective) admissions and a roughly equal number of females and males. Both datasets cover the entire adult age spectrum (USA patients range between 15 and 90 years and ISR patients between 20 and 110), but the ISR cohort is skewed towards older patients (USA median age is 63 and ISR is 73, where 82% of ISR patients are above 60 while only 55% of the USA patients are).
In addition, we obtained records of the Healthcare Cost and Utilization Project (HCUP) of the Nationwide Inpatient Sample (NIS) of 2009 which contains more than 55 million associations between 5.8 million patients and 1,125 third level discharge ICD codes. The latter data were used to enhance the computation of ICD similarities, as described below.
The ICD codes in the EHR data included 469 (USA) and 396 (ISR) third level ICD codes (diagnostic and procedural codes). We excluded supplementary classification codes (codes starting with E or V) and several first level categories including complications of pregnancy (630 to 679) and codes in the range 740 to 999 for being uninformative (for example, general symptoms), a known condition (for example, congenital anomalies) or incidental conditions (for example, injuries or poisoning). We retained supplementary classification codes V40 to V49 –'persons with a condition influencing their health status' for being indicative of procedures a patient underwent.
As a sanity check, we extracted the ICD codes that were enriched in patients with extreme blood test values relative to other patients (hypergeometric test, false discovery rate (FDR) = 0.01) and verified that these corresponded to common knowledge associations, for example, various ICDs coding for cancer are enriched within patients with high lactic dehydrogenase values
Additional file 1: Table S2
ICD codes enriched in extreme valued blood tests.
Click here for file
The patients were de-identified by using a randomly generated patient id. The study was approved by the Institutional Review Board of Stanford and by the Helsinki Committee of the Rabin Medical Center.
Similarity measure construction
In order to infer patient diagnosis, we computed a set of ten patient similarities. We computed two ICD similarity measures (1–2) and eight similarity measures between hospitalizations (3–10). All similarity measures were normalized to the range [0, 1]. We used the following ICD code similarities:
(1) ICD code similarity: We used the levels of the ICD codes in the ICD coding hierarchy to measure the similarity between ICD codes ci and cj as
(2) Empirical co-occurrence frequency: We used the HCUP data to compute empirical co-occurrences between ICD codes. Computing the number of co-occurrences of an ICD pair across all patients, we first computed the Jaccard score
We used the following inter-patient similarity measures
(3–4) Medical history: Each patient may possess medical history from three sources: (i) past encounters with local health providers (digitally connected to the medical center); (ii) discharge codes of past hospitalizations; and (iii) personal history ICD codes provided in the current hospitalization (ICD codes V01to V15, V40 to V49 and V87). The union of these three sources constitutes the patient medical history profile. To compute the similarity of two such profiles, we form a bipartite graph over the member ICD codes, connecting two codes in the two profiles by an edge whose weight is the similarity between the codes. Our similarity score is the value of a maximal matching in this graph normalized by the smaller history set size. We performed the maximal matching computation using either of the two ICD similarity measures, resulting in two similarity measures.
(5–6) Blood test similarity: We used only the chronologically first blood test of each type, performed upon admission for each hospitalization, retaining only blood test results obtained during the first three days of hospitalization. We filtered blood tests that were performed in less than 5% of the hospitalizations and those for which the difference in distribution between patients with the same diagnosis and patients without shared diagnosis was not statistically significant (Wilcoxon ranked sum test, FDR <0.01). This left us with 86 blood tests for the USA dataset and 19 blood tests for the ISR set. Each blood test was then normalized by converting it to a
(7–8) ECG similarity: The ECG values included eight interval values as well as the heart rate. Similarly to the blood tests, we used only the chronologically first measurement, performed upon admission for each hospitalization, obtained during the first three days of hospitalization. Each ECG measurement had undergone the same normalization and similarity construction as the blood tests.
(9) Age similarity: In order to give precedence to age differences in younger age, we computed the similarity between two patients
(10) Gender similarity: defined as 1 if the two patients have the same gender and 0 otherwise.
Combining similarity measures to classification features
The framework we used scores a hypothetical association according to its maximal similarity to a known, gold-standard, set of associations. In our case, we scored associations between hospitalization records and ICD codes based on the highest similarity to the known discharge codes in the background corpus of previously hospitalized patients (disregarding similarities to previous hospitalizations of the same patient). Specifically, the features used to classify hospitalization-primary discharge ICD code pairs were constructed from scores computed for each combination of an ICD-similarity measure and a similarity measure between patient hospitalizations (see previous section for details), resulting in 16 features overall (12 without the ECG similarities). For each such pair of similarity measures, the score of a potential discharge code
Performance evaluation
We used the MATLAB implementation of the logistic regression classifier (glmfit function with binomial distribution and logit linkage) for the prediction task. We used a 10-fold cross validation scheme to evaluate the precision of our prediction algorithm. The training set used for the cross validation included 41,036 USA associations between hospitalizations and discharge codes and 14,506 ISR associations. We considered two types of negative sets, the same size as the positive set in each training set: (i) randomly sampling for each patient a diagnosis from the 469 (USA) or 396 (ISR) third level ICD codes (excluding true diagnoses for that patient), termed 'pre-admission'; and (ii) randomly sampling a set of potential release codes for each hospitalization, termed 'post-admission.' Specifically for the second negative set scenario, we inspected the available admission diagnoses reported upon hospitalization (lacking from the USA dataset) and included the set of discharge diagnoses of all the patients who shared the same admission diagnosis (excluding the true discharge diagnosis for that hospitalization). As an example, the potential negative set for a patient admitted with chest pain includes the discharge diagnoses of all other patients admitted with chest pain, excluding the true final diagnoses of that patient. Additionally, we removed self-similarities of patients (that is, similarities between hospitalizations of the same patient) to avoid bias for patients with recurrent admissions. To obtain robust area under the curve (AUC) score estimates, we performed 10 independent cross validation runs, selecting a different negative set and a different random partition of the training set to 10 parts in each; we then averaged the resulting AUC scores. Expectedly, taking a negative set of size five, ten or twenty times the size of the positive set had a negligible effect on the resulting AUC score (AUC difference less than 0.002).
In order to apply our method in a scenario that mimics the admission of new patients, we split the hospitalizations into training and validation subsets. For the ISR data, we used the available admission date to select hospitalizations that spanned the first year of our data (July 2010 to June 2011) as our training set and validated on hospitalizations occurring in the subsequent 211 days, totaling 999 hospitalizations. For the USA data, we split the data into train and test sets (two thirds and a third, respectively) using the available sequential ordering of their admission dates. As with the cross-validation scheme, we masked similarities between hospitalizations of the same patient. We computed the precision of our predictions by counting the number of patients for which the top predicted discharge code was the same as one of its true diagnoses. Similarly, we also computed the performance when testing whether the true discharge code of a patient appeared in the top two predictions, top three and up to the top ten predictions per patient.
In order to identify ICD codes that are significantly correctly predicted, we compared the number of correct predictions for each ICD code against a background of 105 randomly shuffled patient-diagnosis associations sets.
The inference framework
Our objective was to test whether a minimal amount of patient information, available upon admission in EHRs, can be integrated with a background corpus of previous patients to infer the patient's primary discharge ICD codes (including both diagnoses and procedure codes). The patient information we used for this task includes medical history, the results of the first administered blood and ECG tests and demographics (Methods). To this end, we defined novel diagnosis- and patient-similarity measures, allowing us to exploit the similarity-based inference framework of
Figure 1
A schematic view of the method
A schematic view of the method. Similarities between ICD codes and between hospitalizations are computed (A). A new patient is scored according to the most similar patients with a certain diagnosis (B). A classifier is applied to select the top scoring diagnoses for this patient (C). ICD, International Classification of Diseases.
In order to gain insights about the global properties of the medical history, blood test and ECG similarities, we first examined the networks formed by associating an individual patient with the closest matching patient in the historical database. Interestingly, the networks formed by these similarities show marked differences (consistent across the two EHR datasets). While medical history similarities tend to connect patients into big clusters, blood test and ECG similarities display highly disconnected sub-networks [See Additional file
Additional file 2: Figure S1
Networks of patient similarities. The similarity between patients based on medical history (A), blood test (B) and ECG (C) data.
Click here for file
Additional file 3: Figure S2
The performance of individual features in cross validation. Displayed are individual feature AUC scores for the USA data (Red) and ISR data (blue). The abbreviated feature combinations include: ICD hierarchy-based similarity (I1), ICD empirical similarity (I2), Age (A), Gender (G), blood tests- average difference (BT1), blood tests-difference between extremes (BT2), ECG tests- average difference (ECG1), ECG tests-difference between extremes (ECG2), medical history (MH1) and medical history – empirical ICD similarity based (MH2).
Click here for file
Prediction of discharge ICD-9 codes
We focused on inferring the primary discharge codes for the hospitalization, as they encompass the most crucial piece of information for the caring physician. Due to information content and ICD code usage differences between the two datasets, we train and predict on each dataset independently (see also Discussion for expansion). Our EHR datasets included a set of ranked discharge codes assigned by hospital specialists based on coded and unstructured clinical data in the patient record. We selected a gold standard of 'primary' discharge codes consisting of the two top-ranked discharge codes per patient. In the case of the ISR dataset we added a sparse set of release codes assigned by the physician (accompanying the free-text release notes) totaling 2.2 ± 1.2 codes per patient on average. Overall, our set included 469 and 396 third level ICD diagnostic and procedural codes for the USA and ISR datasets, respectively (Methods).
In order to validate our predictions, we first applied a 10-fold cross validation scheme. In selecting the negative set, we considered two scenarios: (i) sampling of the entire set of false ICD codes (termed 'pre-admission', see Methods); and (ii) a more realistic case, available only in the ISR dataset, in which we sample only from the potential discharge diagnoses that a physician might consider based on the patient admission diagnoses (termed 'post-admission'). We summarize the cross-validation results for several scenarios in Table
Analyzing the contribution of each feature, we observe that the features involving the hierarchy-based ICD similarity outperformed features built with empirical co-occurrence ICD similarity. Analyzing the classification power of each of the inter-patient similarity measures, we found that none was sufficient for obtaining the overall AUC, with blood tests achieving slightly higher results than medical history or ECG as standalones (AUC <0.85, Additional file
Figure 2
AUC scores for ICD level 1 categories
AUC scores for ICD level 1 categories. AUC scores using only the blood test features (red circles), medical history (blue squares), ECG measurements (black diamonds) and all features (dashed green line) are displayed for the USA (A) and ISR (B) datasets across ICD level 1 categories: Infectious And Parasitic Diseases (A), Neoplasms (B), Endocrine, Nutritional And Metabolic Diseases, And Immunity Disorders (C), Diseases Of The Blood And Blood-Forming Organs (D), Mental Disorders (E), Diseases Of The Nervous System And Sense Organs (F), Diseases Of The Circulatory System (G), Diseases Of The Respiratory System (H), Diseases Of The Digestive System (I), Diseases Of The Genitourinary System (J), Diseases Of The Skin And Subcutaneous Tissue (K), Diseases Of The Musculoskeletal System And Connective Tissue (L), Supplementary Classification Of Factors Influencing Health Status And Contact With Health Services (M) and Classification Of Procedures (N). AUC, area under the curve; ECG, electrocardiography; ICD, International Classification of Diseases.
To ensure that our method is not limited to detecting only chronic patients, which we defined as ones for whom the discharge diagnosis appears also in their medical history (including previous hospitalizations), we verified that we achieve a similar performance when applying our method to a set of 9,990 USA or 5,838 ISR hospitalizations which include only non-chronic cases (Table
Prospective validation
Next, we applied our method in a scenario that mimics the admission of new patients. We split the hospitalizations into training and validation subsets, based on admission date when available (Methods). In the following, we report first the USA dataset performance and the ISR performance is provided in parentheses for clarity. As we focus on predicting at least one primary diagnosis per patient, we measure our performance by computing the percentage of patients with at least one correct prediction (that is, precision). While the top predicted discharge code was correct for 18% (17%) of the patients, the top ten predictions contained a correct discharge code for 67% (64%) of the patients. We note that the task here is more challenging than the previous 'cross-validation' one since the latter evaluates a specific set of options for ICD codes (those in the test set) while here we evaluate all possible codes as we have no prior information for a new patient. One reason for the lower precision lies in the fact that discharge diagnosis codes include also 'secondary' discharge codes, ranked lower than the top two discharge codes for a patient. Since the distinction between primary (top discharge codes and physician release codes) and secondary (additional discharge codes) is done manually and is subjective, we also checked the prediction precision relative to the complete set of discharge codes, including both primary codes and secondary codes (the latter not appearing in the training set) to find that our top prediction was correct for 32% of the patients (both datasets) with 84% (89%) of patients with at least one correct hit within the top ten predictions (Figure
Figure 3
Prediction precision for recent hospitalizations
Prediction precision for recent hospitalizations. The prediction precision for primary discharge codes (black) and all discharge codes (blue) for the USA data (circles) and the ISR data (crosses) as a function of the number of top ranked predictions per patient. Precision is measured for ICD level 1 (A), level 2 (B) and level 3 (C). ICD, International Classification of Diseases; ISR, Israel; USA, United States.
As a physician can likely also benefit from a more coarse classification, we checked the precision in predicting the second and first level of the ICD (Figures
Finally, we analyzed the prediction performance over the different diagnoses. Expectedly, we found a high correlation (Pearson correlation, rho = 0.9,
Additional file 4: Figure S3
The precision in predicting ICD codes as a function of the number of patients in the training set for the USA (A) and ISR (B) datasets.
Click here for file
Additional file 5: Table S1
Easy to predict ICD codes. All p-values are FDR corrected.
Click here for file
Additional file 6: Figure S4
Prediction precision for ICD level 1 categories. Precision values (blue) and relative prevalence (red) are displayed for the USA (A) and ISR (B) datasets across ICD level 1 categories: Infectious And Parasitic Diseases (A), Neoplasms (B), Endocrine, Nutritional And Metabolic Diseases, And Immunity Disorders (C), Diseases Of The Blood And Blood-Forming Organs (D), Mental Disorders (E), Diseases Of The Nervous System And Sense Organs (F), Diseases Of The Circulatory System (G), Diseases Of The Respiratory System (H), Diseases Of The Digestive System (I), Diseases Of The Genitourinary System (J), Diseases Of The Skin And Subcutaneous Tissue (K), Diseases Of The Musculoskeletal System And Connective Tissue (L), Supplementary Classification Of Factors Influencing Health Status And Contact With Health Services (M) and Classification Of Procedures (N).
Click here for file
We used patient cohorts from two different hospitals. However, we trained and provided predictions for each dataset independently. This was done for three reasons: (i) combining the two datasets ignores information available in only one dataset (for example ECG data or blood tests that appear in only one set); (ii) the ICD codes, primarily used for billing purposes, are often biased due to the health system used in each country; and (iii) different sources of medical history (that is, outpatient versus inpatient facilities) display lower agreement between patients from different health systems. Indeed, we observed that merging the two datasets degraded the performance to that of the worse performing dataset (ISR, see Table
In order to assess the potential benefits to a clinician, we looked at predictions that could be considered surprising with regard to the admission diagnoses (available in the ISR dataset). We found multiple examples in which the admission diagnosis contained only general symptoms and our method correctly predicted the true discharge diagnosis. We describe here two such examples: (i) a female patient who was admitted with an unspecified anemia (ICD code 285.9) was correctly predicted for cardiac dysrhythmias (427). Irregular heartbeat is one of the many symptoms of anemia but not a predictive one
Finally, analyzing our performance, we note that while our method provided high quality predictions in cross validation, it is likely to display lower performance in predicting conditions that evolve substantially over time and conditions that are rare in the population. We observe that high level ICD categories that achieve relative high precision are typically abundant in our data (above 6% (USA) and 4% (ISR) of the patients), including diseases related to endocrine, circulatory, respiratory and genitourinary systems (Figure
Our results demonstrate that a large corpus of patient data can be exploited to predict the likely discharge diagnoses for a new patient. We introduced a general method for performing such an inference using information from past hospitalizations. Our method computes patient similarity measures and requires a minimal set of such measures, including medical history, blood tests performed upon admission and demographics. It is readily extensible to use the results of other admission information, such as ECG tests, as shown for the USA dataset and potentially, in the future, medical images and patient genomic information (for example, gene expression measurements or single nucleotide polymorphism data).
Our method is a stepping stone for the full exploitation of large population-based data sets. We recognize that the introduction of new decision support modalities requires careful analysis of physician and health-care system workflows and introduction of the information at the most pertinent decision points. However, it is clear that the emerging infrastructure of electronic patient information will provide not only better information about quality of care and guidance for policy but will be able to improve the care of the individual, benefitting from the aggregated information of previous patients.
AUC: Area under the curve; ECG: Electrocardiography; EHR: Electronic health records; FDR: False discovery rate; HCUP: Healthcare Cost and Utilization Project; ICD: International Classification of Diseases.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
AG and RS conceived the paper; AG performed the analysis and wrote the draft; GS obtained the data, and aided in pre-processing; GS, ER, RA and RS participated in the writing of the paper. All authors read and approved the final manuscript.
AG was funded by the NIH grants LM05652 and GM102365. RS was supported by a research grant from the Israel Science Foundation (grant no. 241/11).
Effects of computerized clinical decision support systems on practitioner performance and patient outcomes
Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success
A four-phase model of the evolution of clinical decision support architectures
Effects of computer-based clinical decision support systems on physician performance and patient outcomes
Statistical and knowledge-based approaches to clinical decision-support systems, with an application in gastroenterology
A cost-benefit analysis of electronic medical records in primary care
Information technology and medication safety: what is the benefit?
Review: use of electronic medical records for health outcomes research: a literature review
Interoperability of clinical decision-support systems and electronic health records using archetypes: a case study in clinical trial eligibility
Electronic health records and clinical decision support systems: impact on national ambulatory care quality
Prediction modeling using EHR data: challenges, strategies, and a comparison of machine learning approaches
Pattern Recognition (ICPR), 2012 21st International Conference on. IEEE
Assessing quality using administrative data
Prognostic significance of serum lactate dehydrogenase in malignant lymphoma
Diagnostic efficiency of troponin T measurements in acute myocardial infarction
Nouvelles recherches sur la distribution florale
PREDICT: a method for inferring novel drug indications with application to personalized medicine
Heart failure post-myocardial infarction: a review of the issues
Anemia in the elderly
Fever in myocardial infarction: is it still common, is it still predictive?
Pre-publication history
The pre-publication history for this paper can be accessed here:


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Disabling right-clicking by using JavaScript
It makes sense to do everything you can to protect your copyrighted content. While there is no way to completely stop people from stealing your images, you can make it harder. Disabling right-clicking is one method you can use to deter casual theft.
On one of my websites, I have a pop-up page where I placed gallery with my copyrighted images. I don't want the user to see the menu thats get displayed with the mouse right click. So I created the HTML/JavaScript code that will prevent the default right menu from popping up when the right mouse is clicked on this pop-up page. Now I use it to stop surfers from easily stealing my copyrighted images from my website.
Simply add the following code to the <BODY> section of your web page (Press Ctrl C after selecting code to copy it) :
<script language="JavaScript">
* Disable mouse right-click on page
* By Arthur Gareginyan (firstname.lastname@example.com)
* For full source code, visit http://www.mycyberuniverse.com
document.addEventListener("contextmenu", function(e){
}, false);
The HTML code above have a simple inline JavaScript code:
document.addEventListener("contextmenu", function(e){ e.preventDefault(); }, false);
Mouse right click is disabled in this page. You can test it by right clicking anywhere in this page.
Browser support
Don't do it
The right menu is there for a reason, and it should be left there. Many browser extensions add entries to the right click menu and the user should be able to use it in any page he visits.
The following article better explains why this should not be done and what other actions can be taken to solve common related issues: http://www.sitepoint.com/dont-disable-right-click/
P.S. If really you cannot resist the urge to block it at least do not put a popup saying "no right click allowed".


text:
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Item #1989 Need Assistance?


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Mon., June 4, 2012, 8:38 a.m.
"He's moving like a tremendous machine"
Nine years ago, as the running of the Belmont Stakes approached, I took up an entire Slice column with the transcript of the track announcer's call of Secretariat's mind-blowing victory in that race in 1973. Many regard that Triple Crown finish as one of the all-time most thrilling moments in the annals of sports.
But reaction to that particular column was sharply divided. Some of my readers enjoyed it and told me so. Others, however, regarded it as a throw-away and felt I had broken my compact with them. Or something.
Next year will be the 40th anniversary of Secretariat's Belmont. I think I know how I might fill up a column as that June 9th approaches.
"...Secretariat is all alone. He's out there almost a sixteenth of a mile away from the rest of the horses."
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Saved Me a Small Fortune - West Orange, NJ
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