Understanding Differences in Patient Descriptions of Rheumatoid Arthritis Flares Using OMERACT Core Domains
Bibliographic record
Abstract
Objective Recently, there has been consensus on domains that constitute flares in rheumatoid arthritis (RA); however, variations in patients’ flare descriptions continue to be observed. This study evaluates how demographic and clinical characteristics influence these differences. Methods Participants enrolled in a prospective RA registry completed a qualitative survey that included the open-ended question “What does a flare mean to you?” Responses were categorized into Outcome Measures in Rheumatology (OMERACT) core and research domains. Univariate analyses evaluated demographic and clinical characteristics. Regression analyses determined independent variables associated with flare description variations. Results Among 645 participants, the median Disease Activity Score in 28 joints (DAS28) with C-reactive protein was 2.1 (IQR 1.6-2.9); 58% of the participants reported at least 1 flare in the past 6 months. Participants reported a median of 3 (IQR 2-5) OMERACT domains when describing flares. Fatigue was more commonly noted among females (odds ratio [OR] 6.12;P< 0.001). Older participants were less likely to report emotional distress (OR 0.97;P= 0.03), swollen joints (OR 0.99;P= 0.04), physical function decrease (OR 0.98;P= 0.02), and a general increase in RA symptoms (OR 0.98;P= 0.005). Participants with a higher DAS28 score were less likely to report symptoms of stiffness (OR 0.70;P= 0.009), and those who experienced a flare within the last 6 months were more likely to describe flares as pain (OR 2.53;P< 0.001) and fatigue (OR 2.00;P= 0.007). Conclusion Variations in patients’ flare descriptions can be driven by a patient’s disease activity, the experience of a recent flare, as well as different demographic characteristics, such as age and gender. Understanding the interplay of these characteristics can guide a physician’s approach to the management of patients’ RA flares.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".