Rasch analysis of the Western Ontario Osteoarthritis of the Shoulder index – the Danish version
Bibliographic record
Abstract
PURPOSE: The Western Ontario Osteoarthritis of the Shoulder (WOOS) index is a disease-specific, patient-reported, 19-question survey that measures the quality of life among patients with osteoarthritis (OA). The purpose of this study was to validate the Danish version of WOOS for OA and fractures (FRs) using modern test theory. PATIENTS AND METHODS: The study included 1,987 arthroplasties in 1,943 patients that were reported to the Danish Shoulder Arthroplasty Register between 2006 and 2011. These included 847 OA and 1,140 FR cases. RESULTS: Principal component analysis indicated the unidimensionality of WOOS. The person reliabilities showed a floor-ceiling effect, indicating that a dichotomy was the best fit for the WOOS scale. For OA, WOOS showed good reliability (item and person reliability of 0.98 and 0.76) and good targeting, with a person mean of -0.56 logits. FR also showed good targeting (person mean of -0.08) and good reliability (item and person reliabilities of 1.00 and 0.86, respectively). All WOOS items fit well with the OA sample except items 5 and 6 (pertaining to grinding and the influence of weather). In addition, item 6 showed signs of degrading the scale with an outfit mean square of 2.46. Only item 6 showed a misfit for FR with no sign of scale degradation. The residual principal component analysis confirmed the unidimensionality of FR but not OA. Six items displayed clinically significant differential item functioning between OA and FR. CONCLUSION: Rasch analysis showed that WOOS had a good fit with the Rasch model when used as a dichotomous scale for OA and FR. However, the results were valid only when WOOS was divided into two categories with a threshold of 950 (50% of the maximum score). For the use of WOOS in future clinical research, we recommend that a dichotomous score be reported as a measure of clinical failure in OA and FR.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".