Validation of the Western Ontario Meniscal Evaluation Tool (WOMET) for Patients with a Degenerative Meniscal Tear
Bibliographic record
Abstract
Background: Arthroscopic partial meniscectomy is the most common orthopaedic procedure and is often carried out to treat a degenerative meniscal lesion. The purpose of the present study was to determine the psychometric properties of the Western Ontario Meniscal Evaluation Tool (WOMET) for patients with an arthroscopically verified degenerative me-niscal tear. Methods: Four hundred and eighty-five patients with an arthroscopically verified degenerative meniscal tear were in-cluded. Two groups of patients were formed: one consisted of 385 patients for the purpose of psychometric testing of the WOMET and the other consisted of 100 patients for the assessment of criterion validity. The reliability of the WOMET questionnaire was assessed by determining both internal consistency and test-retest repeatability; for the latter, a subgroup of forty patients completed the form two weeks preoperatively and again on the day of the operation. Validity assessment included determination of content validity (floor and ceiling effects), criterion validity (completion of the WOMET, the Lysholm knee score, and a generic quality-of-life questionnaire by a group of 100 patients), and construct validity (hypothesis testing). Finally, the responsiveness of theWOMETwas determined with two successive assessments (on the day of surgery and six months postoperatively). Results: The WOMET showed acceptable internal consistency, test-retest reliability, floor and ceiling effects, criterion validity (agreement with both Lysholm and 15-D scores), and construct validity (all hypotheses were significant). The
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".