Assessing the Responsiveness of the Persian Version of the Western Ontario Meniscal Evaluation Tool in Patients with Meniscus Injuries
Bibliographic record
Abstract
Background and Objective: Responsiveness is one of the important properties of health-related questionnaires in demonstrating the changes in a patient's clinical conditions before and after therapy. The present study was carried out with the aim of assessing the responsiveness of the Western Ontario Meniscal Evaluation Tool (WOMET) and determining its minimal clinically important difference in patients undergoing physical therapy interventions after meniscus injuries. Methods: This cross-sectional methodological study was performed on 100 patients aged 18-70 years with meniscus injuries who underwent physical therapy interventions. Patients completed WOMET and Knee Injury and Osteoarthritis Outcome Score (KOOS) questionnaires in the first and tenth sessions. The minimum score obtained from the WOMET questionnaire was zero and the maximum was 1600, and the minimum score obtained from the KOOS questionnaire was zero and the maximum was 168. Internal and external responsiveness were the primary outcomes, and effect size tests, ROC curves, and correlation coefficients were used to examine them. The relationship between the WOMET and KOOS questionnaires were considered as secondary outcomes, which were evaluated by calculating the correlation coefficient. Findings: The results of internal responsiveness showed that the standardized response mean for the entire WOMET questionnaire was 0.11 (insignificant) and Cohen's d score for the entire WOMET questionnaire was -1.586 (large). The difference in the mean internal responsiveness between recovered (20%) and unrecovered (80%) patients reached a significant level (p<0.001). This questionnaire had an acceptable external responsiveness; the area under the curve of the ROC curve was greater and equal to 0.7 and the optimal cut-off point was 20.031 (p<0.001). The Pearson correlation coefficient between WOMET and KOOS questionnaires (except the emotions subscale) was moderate to large (0.5-0.8) with p<0.001. Conclusion: The findings of the study showed that the Persian version of the WOMET questionnaire has a high level of responsiveness and is a suitable tool for evaluating the quality of life among patients suffering from meniscus injury.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".