Responsiveness and clinically important differences of the Western Ontario Rotator Cuff (WORC) Index in surgical and non-surgical treatment groups with different follow-up periods: A systematic review and meta-analysis
Bibliographic record
Abstract
Background This study reviews and meta-analyzes the responsiveness and minimal clinically important difference (MCID) of the Western Ontario Rotator Cuff (WORC) Index for various patient populations and treatment durations. Methods A comprehensive search in PubMed, Embase, Web of Science, and CINAHL identified studies on the responsiveness or MCID of the WORC in shoulder conditions. Two authors independently screened articles. Study quality was appraised using COSMIN and GRADE guidelines. Responsiveness was evaluated using anchor-based MCID and distribution-based standardized mean differences (SMDs) computed with Hedges’ g . A random-effect model addressed study variability, and heterogeneity was assessed with the chi-squared test and I ² statistic. Results The 12 studies yielded high-quality evidence supporting the WORC's responsiveness. A meta-analysis of 1326 observations revealed a significant overall effect size (SMD) of 0.91 (95% CI: 0.56 to 1.26; p < 0.0001), with high heterogeneity ( I ² = 91.2%). Subgroup analyses showed larger effect sizes for long-term follow-ups (SMD = 1.28) and surgical treatments (SMD = 1.14). The average MCID was 17 for conservative treatments within six months, 26 for surgical procedures, and 29 for follow-ups over six months. Conclusion The WORC measures improvements in rotator cuff conditions, with varying MCID values for different treatments and durations.
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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 | 0.038 | 0.086 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.054 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".