Rasch analysis of the Knee injury and Osteoarthritis Outcome Score (KOOS): a statistical re‐evaluation
Bibliographic record
Abstract
The knee injury and Osteoarthritis Outcome Score (KOOS), based on the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), is widely used to evaluate subjective outcome in anterior cruciate ligament (ACL) reconstructed patients. However, the validity of KOOS has not been assessed using Rasch analysis. The objective of this study was to evaluate the viability of KOOS as an outcome measure for ACL reconstruction using the partial credit Rasch model. Rasch analysis was applied to 200 KOOS questionnaires completed by patients consecutively tested 20 weeks after ACL reconstruction and subsequent rehabilitation. Rasch analysis showed that of the five proposed subscales in KOOS, only knee-related quality of life (QoL) and sport and recreational related function (Sport/Rec) fulfilled the criteria of a unidimensional measurement scale when applied to these patients. The three subdomains in KOOS extracted from WOMAC did not fulfill these criteria. While the content of KOOS appears to be relevant for knee patients, the psychometric measurement properties of KOOS are insufficient for use on patients 20 weeks subsequent to ACL reconstruction. A new knee measure targeted for these patients could be developed based on the content of KOOS. This study demonstrates that knee measurement instruments constructed for a specific condition cannot necessarily be used on patients with other similar conditions.
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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 | 0.073 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".