The Development and Validation of a Quality of Life-Measurement Tool for Patients With Meniscal Pathology: The Western Ontario Meniscal Evaluation Tool (WOMET)
Bibliographic record
Abstract
OBJECTIVE: To develop and validate a disease-specific, health-related quality of life (HRQOL) index for patients with meniscal pathology. Our hypothesis was that the Western Ontario Meniscal Evaluation Tool (WOMET) would provide adequate reliability, validity, and responsiveness in measuring HRQOL in patients with meniscal tears or who have undergone meniscal repair or resection. STUDY DESIGN: Validation of a measurement tool. SETTING: A tertiary, university-affiliated, sport medicine clinic. PARTICIPANTS: A methodological protocol based on that of Guyatt et al was used to develop the Western Ontario Meniscal Evaluation Tool (WOMET). Patients with meniscal symptomology and in whom magnetic resonance imaging had suggested meniscal pathology were selected from referrals to a sport medicine clinic. Using this cohort, the development of the WOMET proceeded through item generation, reduction, and instrument pretesting. A second cohort of postarthroscopy patients with confirmed meniscal pathology was used to assess the reliability of the WOMET and validate the instrument. RESULTS: The final instrument has 16 items representing the domains of physical symptoms (nine items), sports/recreation/work/lifestyle (four items), and emotions (three items). It demonstrated adequate content and construct validity when compared with other measures. Test-retest reliability was assessed and was found to be high, with an intraclass correlation coefficient of 0.833. The new instrument was also found to be more responsive than other knee measurement tools when assessed in the same cohort. CONCLUSIONS AND CLINICAL RELEVANCE: The WOMET is a disease-specific tool designed to evaluate HRQOL in patients with meniscal pathology. It is therefore put forth as a validated measurement tool to be used in clinical trials evaluating treatments for meniscal pathology. It could also be used as a prospective outcome measure in research or in clinical practice.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".