Translation and psychometric validation of the traditional Chinese version of the Western Ontario Rotator Cuff (WORC) Index
Bibliographic record
Abstract
Purpose Translate the Western Ontario Rotator Cuff (WORC) Index into Traditional Chinese (WORC-TC), validate its psychometric properties, and adapt it to rotator cuff tears (RCTs) in patients who only understand Traditional Chinese.Methods Translation and psychometric validation were performed. A total of 210 patients completed the WORC-TC, Quick Disability of the Arm, Shoulder, and Hand (QuickDASH), 12-item Short-Form Health Survey (SF-12), Numerical Pain Rating Scale (NPRS), and State Anger Scale (SAS) questionnaires. Construct validity and internal consistency were evaluated. Test–retest reliability analyses were conducted on a 30-patient sub-sample.Results The translation and linguistic validation process modified one item. Exploratory factor analysis identified a four-factor structure explaining 66.132% variance. The WORC-TC demonstrated convergent validity with QuickDASH (r = 0.834, p < 0.001), NPRS (r = 0.754, p < 0.001), and SF-12 (r = −0.702, p < 0.001), and divergent validity with the SAS (r = 0.286, p < 0.001). Known-group validity was confirmed, with significant differences between high- and low-pain groups (p < 0.001). The WORC-TC showed satisfactory internal consistency (Cronbach’s alpha: 0.94) and good test–retest reliability (intraclass correlation coefficient: 0.79).Conclusion The WORC-TC can reliably evaluate quality of life in patients with RCTs who exclusively use Traditional Chinese.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".