Cross-cultural translation of the Western Ontario Cuff Index in Chinese and its validation in patients with rotator cuff disorders
Bibliographic record
Abstract
The Western Ontario Rotator Cuff Index (WORC) is a scale designed to evaluate the impact of rotator cuff (RC) disorders on patients’ general quality of life. Our study aims to adapt the WORC for Chinese patients and to assess its reliability, validity, and responsiveness in Chinese patients with RC disorders. First, we developed the Chinese version of the WORC (C-WORC) in a five-step procedure of translation and cross-cultural adaptation. Next, the recruiting patients finished all three rounds of scales of the C-WORC, the Medical Outcomes Study Short-Form 36 (SF-36), and the Oxford Shoulder score (OSS). Then we calculated Cronbach’s alpha, the intra-class correlation coefficient (ICC), Pearson’s or Spearman’s correlation coefficient (r or r s), the effect size (ES), and the standardized response mean (SRM) to evaluate the reliability, validity and responsiveness of the C-WORC, respectively. Overall, 124 patients with RC disorders successfully completed the first two rounds of the scales, and 108 patients completed the last round of the scales. Good or excellent internal consistency (Cronbach’s alpha = 0.872–0.954) was found in the overall scale and subscales of C-WORC, as well as good or excellent test-retest reliability (ICC = 0.828–0.961). Moderate or good correlations (r/r s = 0.472–0.787) were obtained between the physical subscales of the C-WORC and the OSS and the physical subscales of SF-36; the results were also obtained for the emotions subscale of the C-WORC and the mental subscales of SF-36 (r/r s = 0.520–0.713), which, adequately illustrated that good validity was included in the C-WORC. In addition, good responsiveness was also observed in the overall scale and subscales of the C-WORC (ES = 1.57–2.27, SRM = 1.52–2.28). The C-WORC scale is reliable, valid and responsible for the evaluation of Chinese-speaking patients with RC disorders and would be an effective instrument.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".