Translation, validation, and cross-cultural adaption of the Western Ontario Meniscal Evaluation Tool (WOMET) into German
Bibliographic record
Abstract
PURPOSE: The Western Ontario Meniscal Evaluation Tool (WOMET) was developed in order to investigate the health-related quality of life of patients with meniscal pathologies. The aim of the present study was to translate and validate the WOMET into German. METHODS: A standardized forward backward translation of the WOMET into German was first performed. One hundred ninety-two patients with isolated meniscal tears completed the German version of the WOMET as well as the Western Ontario McMasters University Arthritis Index, and the Knee Osteoarthritis Outcome Score. Furthermore, reliability, construct validity, feasibility, internal consistency, ceiling, and floor effects were then calculated. RESULTS: Excellent feasibility (85.4% fully complete questionnaire), internal consistency (Cronbach's α = 0.92), and test-retest reliability (ICC, r = 0.90) were found. The standard error of measurement and the minimal detectable change were ±4.6 and 12.7 points, respectively. All predefined hypothesises were confirmed. No floor or ceiling effects were found. CONCLUSIONS: The presented German version of the WOMET is a valid and reliable tool for investigating the health-related quality of life of German-speaking patients with meniscal pathologies. LEVEL OF EVIDENCE: Cross-sectional study, Level II.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".