Integrating PROMIS Measures in a Treat-to-Target Approach to Standardize Patient-Centered Treatment of Rheumatoid Arthritis
Bibliographic record
Abstract
Objective To evaluate the effect of a patient-centered rheumatoid arthritis (RA) treat-to-target (T2T) disease management approach on patient outcomes and patient satisfaction with care. Methods In this longitudinal, observational pilot study, rheumatologists implemented a modified T2T approach that integrated Patient Reported Outcomes Measurement Information System (PROMIS) measures for depression, fatigue, pain interference, physical function, and social function into RA care. Study participants selected 1 PROMIS domain to target treatment and completed quarterly follow-up assessments. Participants were classified as improved if their Clinical Disease Activity Index (CDAI) changed by > 5 points. Change in PROMIStscores was examined for the group with improved CDAI, and then compared to those with unchanged or worsened CDAI. Satisfaction with care was assessed using multiple measures, including the Functional Assessment of Chronic Illness Therapy–Treatment Satisfaction–Patient Satisfaction Scale. Results The analytical sample (n = 119, median age 57 years, 90.8% female) was split between those with CDAI > 10 (n = 63) and CDAI ≤ 10 (n = 53). At 1 year, there was improvement in CDAI by > 5 points in 66% and 13% of individuals with baseline CDAI > 10 and baseline CDAI ≤ 10, respectively. Across all participants, improvement in CDAI by > 5 points correlated with improvements in the 5 PROMIS domains. Satisfaction with RA treatment also increased. Conclusion The integration of PROMIS measures into the T2T approach for RA care was associated with improvements in disease activity, and improvement in disease activity was associated with improvements in PROMIS measures.
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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 | 0.079 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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".