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Meta-Analyses in Orthopaedic Surgery: A Systematic Review of Their Methodologies

Background: The number and quality of well-designed scientific studies in the orthopaedic literature are limited. The purpose of this review was to determine the methodological qualities of published meta-analyses on orthopaedic-surgery-related topics. Methods: A systematic review of meta-analyses was conducted. A search of the Medline database provided lists of meta-analyses in orthopaedics published from 1969 to 1999. Extensive manual searches of major orthopaedic journals, bibliographies of major orthopaedic texts, and personal files identified additional studies. Of 601 studies identified, forty met the criteria for eligibility. Two investigators each assessed the quality of the studies under blinded conditions, and they abstracted relevant data. Results: More than 50% of the meta-analyses included in this review were published after 1994. We found that 88% had methodological flaws that could limit their validity. The main deficiency was a lack of information on the methods used to retrieve and assess the validity of the primary studies. Regression analysis revealed that meta-analyses authored in affiliation with an epidemiology department and those published in nonsurgical journals were associated with higher scores for quality. Meta-analyses with lower scores for quality tended to report positive findings. The meta-analyses that focused upon fracture treatment and degenerative disease (hip, knee, or spine) had significantly lower mean quality scores than did meta-analyses that examined thrombosis prevention and diagnostic tests (p < 0.05). Conclusions: The majority of meta-analyses on orthopaedic-surgery-related topics have methodological limitations. Limitation of bias and improvement in the validity of the meta-analyses can be achieved by adherence to strict scientific methodology. However, the ultimate quality of a meta-analysis depends on the quality of the primary studies on which it is based. A meta-analysis is most persuasive when data from high-quality randomized trials are pooled.