Development of a Chinese version of the Western Ontario Meniscal Evaluation Tool: cross-cultural adaptation and psychometric evaluation
Bibliographic record
Abstract
BACKGROUND: The Western Ontario Meniscal Evaluation Tool (WOMET) is a questionnaire designed to evaluate the health-related quality of life (HRQOL) of patients with meniscal pathology. Our study aims to culturally adapt and validate the WOMET into a Chinese version. METHODS: We translated the WOMET into Chinese. Then, a total of 121 patients with meniscal pathology were invited to participate in this study. To assess the test-retest reliability, the Chinese version WOMET was completed twice at 7-day intervals by the participants. The construct validity was assessed using Pearson's correlation coefficient or Spearman's correlation to test for correlations among the Chinese version WOMET and the eight domains of Short Form-36 (SF-36), the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and the International Knee Documentation Committee (IKDC) score. Responsiveness was tested by comparison of the preoperative and postoperative scores of the Chinese version WOMET. RESULTS: The test-retest reliability of the overall scale and different domains were all found to be excellent. The Cronbach's α was 0.90. The Chinese version WOMET correlated well with other questionnaires which suggested good construct validity. We observed no ceiling and floor effects of the Chinese version WOMET. We also found good responsiveness for the effect size, and the standardized response mean values were 0.86 and 1.11. CONCLUSIONS: The Chinese version of the WOMET appears to be reliable and valid in evaluating patients with meniscal pathology.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".