Psychometric properties of the OARSI/OMERACT osteoarthritis pain and functional impairment scales: ICOAP, KOOS-PS and HOOS-PS.
Bibliographic record
Abstract
OBJECTIVES: To evaluate the psychometric properties of the OARSI-OMERACT questionnaires in comparison to the existing validated scales. METHODS: Consecutive hip or knee osteoarthritis patients consulting in an orthopedic department were enrolled in the study. Data collected were pain using the Intermittent and Constant Osteoarthritis Pain (ICOAP), a Numeric Rating Scale (NRS), the Western Ontario McMaster Universities' Osteoarthritis Index (WOMAC) pain subscale, the Lequesne pain subscale; functional impairment using the Knee disability and Osteoarthritis Outcome Score-Physical Function Shortform (KOOS-PS), the Hip disability and Osteoarthritis Outcome Score-Physical Function Shortform (HOOS-PS), a NRS, the WOMAC function sub-scale, the Lequesne function subscale. Validity was assessed by calculating the Spearman's correlation coefficient between all the scales. Reliability was assessed in out-patients with stable disease comparing the data collected within 2 weeks using the intra-class correlation coefficient (ICC). Responsiveness was assessed on the data from hospitalised patients prior to and 12 weeks after a total joint replacement (TJR) using the standardised response mean. RESULTS: Three hundred patients (mean age=68 years, females=62%, hip OA=57%) were included. There was a moderate to good correlation between ICOAP, KOOS-PS, HOOS-PS and the WOMAC, NRS and Lequesne scales. Reliability of the ICOAP hip OA HOOS-PS and KOOS-PS was good (ICC range 0.80-0.81) whereas it was moderate for knee ICOAP (ICC=0.65). Responsiveness of the ICOAP, KOOS-PS and HOOS-PS 12 weeks after TJR was comparable to responsiveness of other scales (SRM range: 0.54-1.82). CONCLUSIONS: The psychometric properties of the ICOAP, KOOS-PS and HOOS-PS were comparable to those of the WOMAC, Lequesne and NRS.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".