Chinese version of the Rotator Cuff Quality of Life questionnaire: Cross-cultural adaptation and validation in rotator cuff-impaired patients in Hong Kong
Bibliographic record
Abstract
Purpose: To adapt the Rotator Cuff Quality of Life (RC-QOL) questionnaire into traditional Chinese (Chi-RC-QOL) and to evaluate the validity and reliability in patients suffering from rotator cuff pathologies in Hong Kong. Methods: The Chi-RC-QOL will be constructed using the forward-translation followed by the backward-translation method. Thirty consecutive patients with clinically and radiologically confirmed rotator cuff pathology were recruited. Descriptive statistics will be followed by validity assessment using comparison with the Constant Shoulder (CS) score, University of California Los Angeles (UCLA) Shoulder Rating Scale, and Western Ontario Rotator Cuff (WORC) score. Parametric data will be tested using Pearson’s correlation coefficient for the total scores. Reliability was assessed using a test–retest interval of 30 min. The Cronbach’s α and intraclass correlation coefficient were calculated. Results: Cronbach’s α and internal consistency scores were high for all parts of the Chi-RC-QOL, with Cronbach’s α ranging between 0.89 and 0.98. Internal consistency scores range from 0.82 to 0.92, which can be regarded as an excellent correlation. Test–retest reliability was excellent for all parts of the Chi-RC-QOL with good absolute reliability. Chi-RC-QOL correlates well with the CS score, UCLA Shoulder Rating Scale, and the WORC score, with all being statistically significant. Conclusions: The current study adapted the RC-QOL to traditional Chinese version. The analysis confirmed the validity and reliability of the Chi-RC-QOL questionnaire. Level of Evidence: III.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".