A systematic review of meta-analyses in orthopaedic surgery between 2000 and 2016
Bibliographic record
Abstract
AIMS: The aims of this systematic review were to describe the quantity and methodological quality of meta-analyses in orthopaedic surgery published during the last 17 years. MATERIALS AND METHODS: MEDLINE, EMBASE, and PubMed, between 1 January 2000 and 31 December 2016, were searched for meta-analyses in orthopaedic surgery dealing with at least one surgical intervention. Meta-analyses were included if the interventions involved a human muscle, ligament, bone or joint. RESULTS: A total of 392 meta-analyses met eligibility criteria, for which the mean AMSTAR quality score was 7.1/11. There was a positive correlation between the year of publication and the quality of the meta-analysis (r = 0.238, p < 0.001). Between 2000 and 2011, the mean AMSTAR score corresponded to that of a medium quality review. However, between 2012 and 2016, the mean scores have been consistently equivalent to those of a high-quality review. The number of meta-analyses published increased 10-fold between 2005 and 2014. CONCLUSION: The quantity and quality of meta-analyses in orthopaedic surgery which have been published has increased, reaching a plateau in 2012. Methodological flaws remain to be addressed in future meta-analyses in order to continue increasing the quality of the orthopaedic literature. Cite this article: Bone Joint J 2018;100-B:1270-4.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.144 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.025 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".