Flares in Rheumatoid Arthritis Patients with Low Disease Activity: Predictability and Association with Worse Clinical Outcomes
Bibliographic record
Abstract
OBJECTIVE: To investigate predictors of flare in rheumatoid arthritis (RA) patients with low disease activity (LDA) and to evaluate the effect of flare on 12-month clinical outcomes. METHODS: Patients with RA who were taking disease-modifying antirheumatic drugs and had a stable 28-joint count Disease Activity Score (DAS28) < 3.2 were eligible for inclusion. At baseline and every 3 months, clinical (DAS28), functional [Health Assessment Questionnaire-Disability Index (HAQ-DI), EQ-5D, Functional Assessment of Chronic Illness Therapy Fatigue scale (FACIT-F), Medical Outcomes Study Short Form-36 (SF-36)], serum biomarkers [multibiomarker disease activity (MBDA) score, calprotectin, CXCL10], and imaging data were collected. Flare was defined as an increase in DAS28 compared with baseline of > 1.2, or > 0.6 if concurrent DAS28 ≥ 3.2. Cox regression analyses were used to identify baseline predictors of flare. Biomarkers were cross-sectionally correlated at time of flare. Linear regressions were performed to compare clinical outcomes after 1 year. RESULTS: Of 152 patients, 46 (30%) experienced a flare. Functional disability at baseline was associated with flare: HAQ-DI had an unadjusted HR 1.82 (95% CI 1.20-2.72) and EQ-5D had HR 0.20 (95% CI 0.07-0.57). In multivariate analyses, only HAQ-DI remained a significant independent predictor of flare (HR 1.76, 95% CI 1.05-2.93). At time of flare, DAS28 and its components significantly correlated with MBDA and calprotectin, but correlation coefficients were low at 0.52 and 0.49, respectively. Two-thirds of flares were not associated with a rise in biomarkers. Patients who flared had significantly worse outcomes at 12 months (HAQ-DI, EQ-5D, FACIT-F, SF-36, and radiographic progression). CONCLUSION: Flares occur frequently in RA patients with LDA and are associated with worse disease activity, quality of life, and radiographic progression. Higher baseline HAQ-DI was modestly predictive of flare, while biomarker correlation at the time of flare suggests a noninflammatory component in a majority of events.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".