221. EARLY ONSET OF BENEFIT BY PATIENT-REPORTED OUTCOMES WITH SARILUMAB TREATMENT IN RHEUMATOID ARTHRITIS
Bibliographic record
Abstract
Background: Efficacy, including improvements in patient-reported outcomes (PROs), was demonstrated in 2 phase 3 trials, MOBILITY (NCT01061736) and TARGET (NCT01709578), of sarilumab, an investigational human anti-interleukin 6 receptor monoclonal antibody. The most common treatment-emergent adverse events in both studies were infections, neutropenia, injection site reactions, and increased transaminases. The objective was to evaluate the early onset of benefit of sarilumab treatment measured by PROs. Methods: In both studies, adults with moderate-to-severe, active rheumatoid arthritis (RA) were randomized to 1 of 3 groups receiving placebo, sarilumab 150 mg, or sarilumab 200 mg subcutaneously (SC) every 2 weeks (q2w) plus methotrexate (MTX; MOBILITY) or conventional synthetic disease-modifying antirheumatic drugs (csDMARDs; TARGET). PROs assessed as early as week 2 included patient global assessment of disease activity (PtGA), pain assessed by visual analogue scale (VAS), Health Assessment Questionnaire-Disability Index (HAQ-DI), Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F), sleep by VAS (MOBILITY), and AM stiffness by VAS (TARGET). Changes from baseline were analyzed using mixed-model repeated measures with region, number of prior tumor necrosis factor inhibitors (TNFis) (TARGET) or prior biologic use (MOBILITY), visit, treatment, treatment-by-visit interaction, and baseline PRO scores as covariates.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".