187 Danish version of the western Ontario meniscal evaluation tool (WOMET): a crosscultural adaptation, test-retest reliability and responsiveness study
Bibliographic record
Abstract
Introduction The Western Ontario Meniscal Evaluation Tool (WOMET) is designed to evaluate Health Related Quality of Life (HRQOL) in patients with meniscal injuries. The purpose of this study was to translate and crossculturally adapt the WOMET for use in Danish and evaluate its reliability and responsiveness. Materials and Methods The WOMET was forward and backward translated into Danish according to international guidelines. 60 patients (mean age 49 years (range 19–71 years), 57% females) with meniscal injury scheduled for arthroscopy meniscal surgery in the period from September 2017 to February 2018, were included in this study. The WOMET was completed at baseline, 3- and 6-months post-surgery. Additionally, test- retest reliability was assessed at 3-months in 55 patients with stable symptom state from test to retest. Responsiveness was assessed between the WOMET and The Knee injury and Osteoarthritis Outcome Score (KOOS4 – aggregate of 4 of 5 KOOS-subscales). Results The Danish version of the WOMET was successfully translated and showed good face validity. Test-retest reliability was excellent, with Intra Class Correlation (ICC) of 0.88 (95%CI 0.84–0.92) for the total score. The Standard Error of Measurement (SEM) was 125 points and the Minimal Detectable Change (MDC) was 347 points (7.8% and 21.7% of the total score, respectively. The WOMET had good responsiveness with an effect size (ES) of 1.12 at 6 months post-surgery, which was comparable to the KOOS4 (ES 1.10). Conclusion The Danish version of the WOMET is reliable and responsive for assessing health-related quality of life in patients with meniscal pathology.
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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 | 0.009 |
| 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 | 0.000 |
| 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".