Measuring Flares in Rheumatoid Arthritis. (Why) Do We Need Validated Criteria?
Bibliographic record
Abstract
In the treatment of rheumatoid arthritis (RA) it is of growing importance to measure disease activity both in clinical practice as well as in research. The last 20 years have brought us several well-validated disease activity indices: for example, the Disease Activity Score (DAS), DAS28 for 28 joints, the Clinical Disease Activity Index, and the Simplified Disease Activity Index are currently being used, and validated cutoff points to determine disease activity states as well as change criteria to indicate improvement to therapy have been developed1,2,3,4,5,6. However, in addition to measuring absolute disease activity states and improvement, there is an increasing need for assessing RA flare or worsening. Therefore, at the OMERACT 9 (Outcomes in Rheumatology) meeting a working definition of RA flare was proposed: flare occurs with any worsening of (or return of) disease activity that would, if persistent, lead to (re)initiation, increase or/and change of therapy; a flare represents a cluster of symptoms of sufficient duration intensity to require (re)initiation, change, or increase in therapy1. Although this working definition was an essential first step, research is needed on validated flare criteria, and the work of Bykerk, et al in this issue of The Jou r nal represents an important contribution in the field7. Here, we would like to discuss several aspects of development and use of RA flare criteria. First, why do we need thoroughly validated flare criteria? The first scenario that exemplifies the need for a flare criterion is the use of fire-and-forget type of treatments such as rituximab, in which the timing of retreatment is often based on occurrence of a worsening in disease activity. Flare criteria are also essential in down-titration and discontinuation studies as well as … Address correspondence to Dr. van der Maas, Sint Maartenskliniek, Department of Rheumatology, Hengstdal 3, 6522 JV, Nijmegen, The Netherlands. E-mail: a.vandermaas{at}maartenskliniek.nl
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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 | 0.126 | 0.234 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".