Reliability and responsiveness of measures of pain in people with osteoarthritis of the knee: a psychometric evaluation
Bibliographic record
Abstract
PURPOSE: To examine the fit between data from the Short Form McGill Pain Questionnaire (SF-MPQ-2) and the Rasch model, and to explore the reliability and internal responsiveness of measures of pain in people with knee osteoarthritis. METHODS: Participants with knee osteoarthritis completed the SF-MPQ-2, Intermittent and Constant Osteoarthritis Pain questionnaire (ICOAP) and painDETECT. Participants were sent the same questionnaires 3 and 6 months later. RESULTS: Fit to the Rasch model was not achieved for the SF-MPQ-2 Total scale. The Continuous subscale yielded adequate fit statistics after splitting item 10 on uniform DIF for gender, and removing item 9. The Intermittent subscale fit the Rasch model after rescoring items. The Neuropathic subscale had relatively good fit to the model. Test-retest reliability was satisfactory for most scales using both original and Rasch scoring ranging from fair to substantial. Effect sizes ranged from 0.13 to 1.79 indicating good internal responsiveness for most scales. CONCLUSIONS: These findings support the use of ICOAP subscales as reliable and responsive measure of pain in people with knee osteoarthritis. The MPQ-SF-2 subscales found to be acceptable alternatives. Implications for Rehabilitation The McGill Pain Questionnaire short version 2 is not a unidimensional scale in people with knee osteoarthritis, whereas three of the subscales are unidimensional. The McGill Pain Questionnaire short version 2 Affective subscale does not have good measurement properties for people with knee osteoarthritis. The McGill Pain Questionnaire short version 2 and the Intermittent and Constant Osteoarthritis Pain scales can be used to assess change over time. The painDETECT performs better as a screening measure than as an outcome measure.
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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 | 0.020 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".