Evaluation of different methods used to assess disease activity in rheumatoid arthritis: analyses of abatacept clinical trial data
Bibliographic record
Abstract
OBJECTIVES: To evaluate different methods of reporting response to treatment or disease status for their ability to discriminate between active therapy and placebo, or to reflect structural progression or patient satisfaction with treatment using an exploratory analysis of the Abatacept in Inadequate Responders to Methotrexate (AIM) trial. METHODS: 424 active (abatacept approximately 10 mg/kg) and 214 placebo-treated patients with rheumatoid arthritis (RA) were evaluated. METHOD: of reporting included: (1) response (American College of Rheumatology (ACR) criteria) versus state (disease activity score in 28 joints (DAS28) criteria); (2) stringency (ACR20 vs 50 vs 70; moderate disease activity state (MDAS; DAS28 <5.1) vs low disease activity state (LDAS; DAS28 or=2). More stringent criteria (at least ACR50/LDAS), faster onset ( 3 visits) of ACR50/LDAS best reflected patient satisfaction (positive LR >10). CONCLUSIONS: The optimal method for reporting a measure of disease activity may differ depending on the outcome of interest. Time to onset and sustainability can be important factors when evaluating treatment response and disease status in patients with RA.
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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 | 0.197 | 0.268 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".