Feasibility and Face Validity of Outcome Measures for Use in Future Studies of Polymyalgia Rheumatica: An OMERACT Study
Bibliographic record
Abstract
OBJECTIVE: To survey participants with polymyalgia rheumatica (PMR) to evaluate the face validity, acceptability, and domain match of proposed candidate outcome measures. METHODS: A structured, online, anonymous survey was disseminated by patient support groups through their networks and online forums. The candidate outcome measures comprised (1) visual analog scale (VAS) and numerical rating score (NRS) to assess pain; (2) VAS, NRS, and duration to assess stiffness; (3) the modified Health Assessment Questionnaire and Health Assessment Questionnaire Disability Index to assess physical function; and (4) C-reactive protein and erythrocyte sedimentation rate to assess inflammation. Free-text answers were analyzed using descriptive thematic analysis to determine respondents' views of the candidate instruments. RESULTS: Seventy-eight people with PMR from 6 countries (UK, France, USA, Canada, Australia, and New Zealand) participated in the survey. Most respondents agreed candidate instruments were acceptable or "good to go." Free-text analysis identified 5 themes that participants considered inadequately covered by the proposed instruments. These related to (1) the variability, context, and location of pain; (2) the variability of stiffness; (3) fatigue; (4) disability; and (5) the correlation of inflammatory marker levels and severity of symptoms, sometimes reflecting disease activity and other times not. CONCLUSION: Participants reported additional aspects of their experience that are not covered by the proposed instruments, particularly for the experience of stiffness and effect of fatigue. New patient-reported outcome measures are required to increase the relevance of results from clinical trials to patients with PMR.
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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 | 0.122 | 0.222 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".