Measuring Disease Exacerbation and Flares in Rheumatoid Arthritis: Comparison of Commonly Used Disease Activity Indices and Individual Measures
Bibliographic record
Abstract
OBJECTIVE: To evaluate and compare the utility of commonly used outcome measures for assessing disease exacerbation or flare in patients with rheumatoid arthritis (RA). METHODS: Data from the Dutch Potential Optimalisation of (Expediency) and Effectiveness of Tumor necrosis factor-α blockers (POET) study, in which 462 patients discontinued their tumor necrosis factor-α inhibitor, were used. The ability of different measures to discriminate between those with and without physician-reported flare or medication escalation at the 3-month visit (T2) was evaluated by calculating effect size (ES) statistics. Responsiveness to increased disease activity was compared between measures by standardizing change scores (SCS) from baseline to the 3-month visit. Finally, the incremental validity of individual outcome measures beyond the Simplified Disease Activity Score was evaluated using logistic regression analysis. RESULTS: The SCS were greater for disease activity indices than for any of the individual measures. The 28-joint Disease Activity Score, Clinical Disease Activity Index, and Simplified Disease Activity Index performed similarly. Pain and physician's (PGA) and patient's global assessment (PtGA) of disease activity were the most responsive individual measures. Similar results were obtained for discriminative ability, with greatest ES for disease activity indices followed by pain, PGA, and PtGA. Pain was the only measure to demonstrate incremental validity beyond SDAI in predicting 3-month flare status. CONCLUSION: These results support the use of composite disease activity indices, patient-reported pain and disease activity, and physician-reported disease activity for measuring disease exacerbation or identifying flares of RA. Physical function, acute-phase response, and the auxiliary measures fatigue, participation, and emotional well-being performed poorly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.000 | 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".