Rasch Analysis for the Knee Injury and Osteoarthritis Outcome Score Joint Replacement Version in Individuals Awaiting Total Knee Replacement Surgery
Bibliographic record
Abstract
The aim of this study was to verify the single-factor structure of the joint replacement version of the Knee Injury and Osteoarthritis Outcome Score (KOOS-JR) and examine its measurement properties in the context of Rasch analysis in patients with end-stage osteoarthritis of the knee (KOA) awaiting total knee replacement (TKR). The study design was retrieval of prospectively collected clinical data. The data were extracted from the presurgery visit for individuals with KOA who were scheduled for primary TKR at a tertiary care hospital. Those who were scheduled for revision of TKR had any other lower extremity injury or surgery during 6 months prior to the presurgery visit, or those who had reported pre-existing neurological impairments affecting the lower extremity functions were excluded during data extraction. The assumptions of Rasch analysis that were examined included the test of fit, fit of residuals, ordering of item thresholds, Pearson separation index, differential item functioning (DIF), dependency, and unidimensionality. The main outcome measure was KOOS-JR. Data were extracted for 283 patients, including 112 men and 160 women, from clinical charts. The KOOS-JR demonstrated good overall fit to the Rasch model. However, it failed to meet the assumption of unidimensionality. None of the items demonstrated DIF or concerns with response thresholds. Person-item threshold distribution indicated that the score for KOOS-JR overestimated person traits with floor and ceiling effects. Reliability statistics were equal to 0.9, suggesting that seven items within the KOOS-JR were internally consistent and reliable. The hypothetical unidimensional KOOS-JR could not be reproduced in our sample in that KOOS-JR had a latent construct. Future research should perform exploratory factor analysis to examine this latent construct.
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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 | 0.022 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".