Patient Reported Outcome Measures for Rheumatoid Arthritis Disease Activity: Rasch measurement theory to identify items and domains
Bibliographic record
Abstract
OBJECTIVES: Disease activity (DA) monitoring is a standard of care in RA. There is demand for achieving this through patient-reported outcome measures (PROMs). The aim of this study was to determine which items could be used to measure the construct of RA DA, by analysing legacy PROMs, using Rasch measurement theory (RMT) analyses. METHODS: Questionnaires including 10 legacy PROMs were sent to people with RA to create original and validation datasets. Items were grouped according to OMERACT domains and analysed using principal component analysis. Based on separate domain RMT analyses of the original dataset, domain-level testlets were assessed to determine which items measure the construct of RA DA. The result was then replicated in confirmatory factor analyses bifactor models and RMT analyses of the validation dataset. Psychometric properties of legacy PROMs were also assessed in the original dataset. RESULTS: The total sample size was 691 (original: 398, validation: 293). The Patient global domain was split into General health and Disease activity domains under RMT. General health and Fatigue domain items measure a separate construct to the construct of RA DA. A set of 12 Pain, Disease activity, Tenderness and swelling, Physical functioning and Stiffness domain items can be used to measure the construct of RA DA. No legacy PROMs fully fit the Rasch measurement model. CONCLUSION: General health and Disease activity domain items are not interchangeable. Twelve items form an item pool that can be used to measure the construct of RA DA. Legacy PROMs should not be recommended for use.
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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 | 0.035 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".