Knee-Specific Quality-of-Life Instruments
Bibliographic record
Abstract
BACKGROUND: Knee-specific quality-of-life instruments are commonly used outcome measures. However, they have not been compared for their ability to detect symptoms and disabilities important to patients. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 1. METHODS: Subjective portions of 11 knee-specific instruments were consolidated. The frequency and importance of each item were assessed. One hundred fifty-three patients with anterior cruciate ligament ruptures, isolated meniscal tears, or osteoarthritis were polled. Instruments were ranked according to the number of items with high mean importance, high frequency importance product, and low mean importance, and according to the number endorsed by at least 51% of patients. RESULTS: For anterior cruciate ligament tears, the Mohtadi quality-of-life instrument scored highest in 3 categories. For meniscal tears, the Western Ontario Meniscal Evaluation Tool scored highly in all 4 categories. For osteoarthritis, the Western Ontario and McMaster Universities Osteoarthritis Index scored highly in 4 categories. Of the general knee instruments, the International Knee Documentation Committee Standard Evaluation Form and the Knee Injury and Osteoarthritis Outcome Score scored favorably. CONCLUSION: The Mohtadi quality-of-life instrument, Western Ontario Meniscal Evaluation Tool, and Western Ontario and McMaster Universities Osteoarthritis Index-disease-specific instruments-contain many items important to patients. Of general knee instruments studied, the International Knee Documentation Committee Standard Evaluation Form and the Knee Injury and Osteoarthritis Outcome Score contain the most items important to patients. CLINICAL RELEVANCE: This study guides clinicians and researchers in selecting instruments that ensure that the patient's perspective is considered for outcome studies involving 3 common knee disorders.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".