Identifying Preliminary Domains to Detect and Measure Rheumatoid Arthritis Flares: Report of the OMERACT 10 RA Flare Workshop
Bibliographic record
Abstract
BACKGROUND: While disease flares in rheumatoid arthritis (RA) are a recognized aspect of the disease process, there is limited formative research to describe them. METHODS: The Outcome Measures in Rheumatology Clinical Trials (OMERACT) RA Flare Definition Working Group is conducting an international research project to understand the specific characteristics and impact of episodic disease worsening, or "flare," so that outcome measures can be developed or modified to reflect this uncommonly measured, but very real and sometimes disabling RA disease feature. Patient research partners provided critical insights into the multidimensional nature of flare. The perspectives of patients and healthcare and research professionals are being integrated to ensure that any outcome measurement to detect flares fulfills the first OMERACT criteria of Truth. Through an iterative data-driven Delphi process, a preliminary list of key domains has been identified to evaluate flare. RESULTS: At OMERACT 10, consensus was achieved identifying features of flare in addition to the existing core set for RA, including fatigue, stiffness, symptom persistence, systemic features, and participation. Patient self-report of flare was identified as a component of the research agenda needed to establish criterion validity for a flare definition; this can be used in prospective studies to further evaluate the Discrimination and Feasibility components of the OMERACT filter for a flare outcome measure. CONCLUSION: Our work to date has provided better understanding of key aspects of the RA disease process as episodic, potentially disabling disease worsening even when a patient is in low disease activity. It also highlights the importance of developing ways to enhance communication between patients and clinicians and improve the ability to achieve "tight control" of disease.
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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 | 0.110 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".