Probability of Response in the First Sixteen Weeks After Starting Biologics: An Analysis of Juvenile Idiopathic Arthritis Biologics Trials
Bibliographic record
Abstract
OBJECTIVES: Most juvenile idiopathic arthritis (JIA) biologic disease-modifying antirheumatic drugs (bDMARDs) trials used an open-label run-in period followed by randomized medication withdrawal. We used data from the run-in period of 4 bDMARD trials to 1) delineate early response trajectory to bDMARDs and 2) identify predictors of early response. METHODS: Data from the first 16 weeks of 4 bDMARD trials were used. The primary outcome was the American College of Rheumatology (ACR) Pediatric 50 (Pedi 50) response criteria: clinically significant response defined as ACR Pedi 50 or greater. The secondary outcome was the clinical Juvenile Arthritis Disease Activity Score in 10 joints (cJADAS10) minimal disease activity state. Response transition rates and predictors were modeled using an inhomogeneous Markov multistate model. RESULTS: Five hundred thirty-two participants (70% receiving methotrexate, 41% prednisone) were included. By month 4, the probability of attaining ACR Pedi 50 or greater was 0.698. If ACR Pedi 50 or more was not achieved by month 1, the probability of achieving it by month 4 was 0.60. If ACR Pedi 50 or more was not achieved by month 3, the probability of achieving this by month 4 was 0.31. Age at diagnosis, disease duration, baseline rheumatoid factor, and active joint counts predicted ACR and cJADAS state transitions, adjusted for concomitant treatment. CONCLUSIONS: No response ACR Pedi 50 or more by month 1 after treatment was associated with a 0.60 probability of responding by month 4, but not responding by month 3 was associated with a 0.31 probability of response by month 4. Baseline disease duration, rheumatoid factor, and active joint counts predicted early treatment response (ACR and cJADAS10 states).
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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 | 0.082 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".