The translated Danish version of the Western Ontario Meniscal Evaluation Tool (WOMET) is reliable and responsive
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to translate and cross-culturally adapt the Western Ontario Meniscal Evaluation Tool (WOMET) for use in Denmark and evaluate its test-retest reliability and comparative responsiveness. METHODS: Sixty patients (mean age 50 years (range 19-71 years), females 57%) with meniscal injury scheduled for arthroscopic meniscal surgery at a small Danish hospital in the period from September 2017 to February 2018 were included in this study. The WOMET was translated into Danish using forward and backward translation. The WOMET was completed at baseline (pre-surgery), at 3 and 6 months postoperatively. Additionally, reliability was assessed at 3 months and 3 months plus 1 week, for patients with a stable symptom state (global response question) between test and retest. Comparative responsiveness was assessed between the WOMET and the Knee Injury and Osteoarthritis Outcome Score (KOOS4-aggregate score of 4 of the 5 KOOS subscales). RESULTS: The Danish version of WOMET showed excellent test-retest reliability, intraclass correlation coefficient of 0.88 (95% CI 0.84-0.92) for the total score. The standard error of measurement was 125 points and the minimal detectable change was 347 points (i.e. 8% and 22% of the total score, respectively). The WOMET was responsive with an effect size (ES) of 1.12 at 6 months after surgery, which was comparable to the KOOS4 (ES 1.10). CONCLUSION: The Danish version of the WOMET is a reliable and responsive measure of health-related quality of life in patients with meniscal pathology. LEVEL OF EVIDENCE: 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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".