Feasibility and Construct Validation of the Patient Reported Outcomes Measurement Information System in Systemic Vasculitis
Bibliographic record
Abstract
OBJECTIVE: The Patient Reported Outcome Measurement Information System (PROMIS) is a collection of item banks of self-reported health. This study assessed the feasibility and construct validity of using PROMIS instruments in vasculitis. METHODS: Data from a multicenter longitudinal cohort of subjects with systemic vasculitis were used. Instruments from 10 PROMIS item banks were selected with direct involvement of patients. Subjects completed PROMIS instruments using computer adaptive testing (CAT). The Medical Outcomes Study Short Form-36 (SF-36) was also administered. Cross-sectional construct validity was assessed by calculating correlations of PROMIS scores with SF-36 measures and physician and patient global scores for disease activity. Longitudinal construct validity was assessed by correlations of between-visit differences in PROMIS scores with differences in other measures. RESULTS: During the study period, 973 subjects came for 2306 study visits and the PROMIS collection was completed at 2276 (99%) of visits. The median time needed to complete each PROMIS instrument ranged from 40 to 55 s. PROMIS instruments correlated cross-sectionally with individual scales of the SF-36, most strongly with subscales of the SF-36 addressing the same domain as the PROMIS instrument. For example, PROMIS fatigue correlated with both the physical component score (PCS; r = -0.65) and with the mental component score (MCS; r = -0.54). PROMIS physical function correlated strongly with PCS (r = 0.81) but weakly with MCS (r = 0.29). Weaker correlations were observed longitudinally between change in PROMIS scores with change in PCS and MCS. CONCLUSION: Collection of data using CAT PROMIS instruments is feasible among patients with vasculitis and has some cross-sectional and longitudinal construct validity.
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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 | 0.080 | 0.125 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".