Outcome Measures Used in Arthroplasty Trials: Systematic Review of the 2008 and 2013 Literature
Bibliographic record
Abstract
OBJECTIVE: Previously published literature assessing the reporting of outcome measures used in joint replacement randomized controlled trials (RCT) has revealed disappointing results. It remains unknown whether international initiatives have led to any improvement in the quality of reporting and/or a reduction in the heterogeneity of outcome measures used. Our objective was to systematically assess and compare primary outcome measures and the risk of bias in joint replacement RCT published in 2008 and 2013. METHODS: We searched MEDLINE, EMBASE, and CENTRAL for RCT investigating adult patients undergoing joint replacement surgery. Two authors independently identified eligible trials, extracted data, and assessed risk of bias using the Cochrane tool. RESULTS: Seventy RCT (30 in 2008, 40 in 2013) met the eligibility criteria. There was no significant difference in the number of trials judged to be at low overall risk of bias (n = 6, 20%) in 2008 compared with 2013 [6 (15%); chi-square = 0.302, p = 0.75]. Significantly more trials published in 2008 did not specify a primary outcome measure (n = 25, 83%) compared with 18 trials (45%) in 2013 (chi-square = 10.6316, p = 0.001). When specified, there was significant heterogeneity in the measures used to assess primary outcomes. CONCLUSION: While less than a quarter of trials published in both 2008 and 2013 were judged to be at low overall risk of bias, significantly more trials published in 2013 specified a primary outcome. Although this might represent a temporal trend toward improvement, the overall frequency of primary outcome reporting and the wide heterogeneity in primary outcomes reported remain suboptimal.
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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 | 0.055 | 0.240 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.024 | 0.023 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".