Reliability and Validity of Selected PROMIS Measures in People with Rheumatoid Arthritis
Bibliographic record
Abstract
PURPOSE: To evaluate the reliability and validity of 11 PROMIS measures to assess symptoms and impacts identified as important by people with rheumatoid arthritis (RA). METHODS: Consecutive patients (N = 177) in an observational study completed PROMIS computer adapted tests (CATs) and a short form (SF) assessing pain, fatigue, physical function, mood, sleep, and participation. We assessed test-test reliability and internal consistency using correlation and Cronbach's alpha. We assessed convergent validity by examining Pearson correlations between PROMIS measures and existing measures of similar domains and known groups validity by comparing scores across disease activity levels using ANOVA. RESULTS: Participants were mostly female (82%) and white (83%) with mean (SD) age of 56 (13) years; 24% had ≤ high school, 29% had RA ≤ 5 years with 13% ≤ 2 years, and 22% were disabled. PROMIS Physical Function, Pain Interference and Fatigue instruments correlated moderately to strongly (rho's ≥ 0.68) with corresponding PROs. Test-retest reliability ranged from .725-.883, and Cronbach's alpha from .906-.991. A dose-response relationship with disease activity was evident in Physical Function with similar trends in other scales except Anger. CONCLUSIONS: These data provide preliminary evidence of reliability and construct validity of PROMIS CATs to assess RA symptoms and impacts, and feasibility of use in clinical care. PROMIS instruments captured the experiences of RA patients across the broad continuum of RA symptoms and function, especially at low disease activity levels. Future research is needed to evaluate performance in relevant subgroups, assess responsiveness and identify clinically meaningful changes.
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".