Continual Maintenance of Remission Defined by the ACR/EULAR Criteria in Daily Practice Leads to Better Functional Outcomes in Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To evaluate longterm functional outcomes in rheumatoid arthritis (RA) based on the number of times that the American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) or the 28-joint Disease Activity Score (DAS28) remission criteria were fulfilled. METHODS: Patients with RA who participated in all 6 data collections in an observational cohort from 2008 to 2010 and who fulfilled the DAS28 remission criteria at baseline were studied. Patients were classified by the number of times they fulfilled the ACR/EULAR [Boolean trial, Boolean practice, Simplified Disease Activity Index (SDAI), or Clinical Disease Activity Index (CDAI)] or DAS28 remission criteria at each collection. The OR for the Japanese version of the Health Assessment Questionnaire (J-HAQ) progression, based on the number of times each set of remission criteria was fulfilled, were calculated by logistic regression. RESULTS: A total of 915 patients were studied. The OR (95% CI) for J-HAQ progression were 0.54 (0.33-0.87), 0.55 (0.33-0.92), 0.48 (0.28-0.82), 0.29 (0.16-0.51), 0.24 (0.13-0.47), and 0.07 (0.03-0.15) for those fulfilling the Boolean trial remission from 1 to 6 times. This tendency was also observed for the other 4 criteria. The OR (95% CI) for J-HAQ progression in patients who achieved remission at all 6 data collections were 0.07 (0.03-0.14) for the Boolean practice, 0.10 (0.05-0.20) for the SDAI, and 0.07 (0.04-0.15) for the CDAI, whereas 0.15 (0.08-0.29) for the DAS28. CONCLUSION: Continual fulfillment of any remission criteria was strongly effective in preventing patients from progression of functional disability; however, the ACR/EULAR criteria appear to be preferable.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".