The Munich Knee Questionnaire: Development and Validation of a New Patient‐Reported Outcome Measurement Tool for Knee Disorders
Bibliographic record
Abstract
PURPOSE: To develop and validate an all-purpose patient-reported outcome questionnaire for a patient-based follow-up examination regarding knee disorders. METHODS: Each scale of the Knee Injury and Osteoarthritis Outcome Score (KOOS), International Knee Documentation Committee (IKDC) score, Lysholm knee score, Western Ontario Meniscal Evaluation Tool (WOMET) score, and Tegner score was analyzed, and after matching of the general topics, the dedicated items underwent a fusion to the final Munich Knee Questionnaire (MKQ) item and a score comprising 33 items was created. In a prospective clinical study, we evaluated validity, reliability, and responsiveness in 152 physical active patients (75 women and 77 men; mean age, 47 years) with traumatic as well as degenerative knee disorders. RESULTS: Test-retest reliability was substantial, with intraclass correlation coefficients of at least 0.91. Construct validity and responsiveness were confirmed by correlation coefficients of 0.78 to 0.86 (P = .01) and 0.41 to 0.71, respectively. Correlation coefficients of the original scores (KOOS, IKDC, Lysholm, WOMET, and Tegner) and the scores calculated from the MKQ were between 0.80 and 0.91 (P = .01). CONCLUSIONS: The MKQ is a reliable and valid patient-reported outcome questionnaire for assessing knee function. It seems to enable the calculation of the original items of the KOOS, IKDC score, Lysholm knee score, WOMET score, and Tegner score. CLINICAL RELEVANCE: The MKQ facilitates the comparison of treatment results in knee disorders and allows the evaluation of treatment efficacy. Identified inadequate treatment concepts could be eliminated, leading to increased patient satisfaction and optimized quality of health care.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".