The Western Ontario Meniscal Evaluation Tool Translated Into Italian Is a Reliable, Precise, and Responsive Patient‐Reported Outcome Measure for Arthroscopic Meniscal Surgery
Bibliographic record
Abstract
Purpose To translate and culturally adapt the Western Ontario Meniscal Evaluation Tool (WOMET) into Italian to examine its reliability, measurement precision, and responsiveness in patients undergoing arthroscopic meniscal surgery. Methods Patients with magnetic resonance imaging–confirmed meniscal injuries completed the Italian WOMET at baseline and again at 3 and 6 months postoperatively. The translation followed established guidelines for cross‐cultural adaptation, including forward‐backward translation and cognitive debriefing. Test‐retest reliability was assessed using the intraclass correlation coefficient, and measurement precision was evaluated by calculating the standard error of measurement and the minimal detectable change. Responsiveness was measured via the standardized response mean. The Knee Injury and Osteoarthritis Outcome Score 4 questionnaire was administered for comparison. Results A total of 97 patients (mean age, 38 years; age range, 22‐58 years) were included. The Italian WOMET showed excellent test‐retest reliability (intraclass correlation coefficient, 0.87). The standard error of measurement was 109.68 points, and the minimal detectable change was 307 points, indicating a high level of precision for detecting true clinical changes. The standardized response means were 1.94 at 3 months and 2.44 at 6 months, indicating strong responsiveness. A high correlation ( r = 0.85, P < .001) with the Knee Injury and Osteoarthritis Outcome Score 4 supported concurrent validity. Conclusions The Italian WOMET is a reliable, precise, and highly responsive patient‐reported outcome measure for assessing health‐related quality of life in patients undergoing arthroscopic meniscal surgery. Clinical Relevance Given the increasing prevalence of meniscal injuries in Italy and the need for culturally relevant diagnostic tools, the validation of the WOMET in Italian is important for patients, health care providers, and scientists.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".