Analysis of Integrated Radiographic Data From Two Long‐Term, Open‐Label Extension Studies of Adalimumab for the Treatment of Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: A longitudinal integration approach evaluated all radiographic scores assessed over 10 years, rather than only completer data, from 2 studies of adalimumab (ADA) for rheumatoid arthritis (RA). METHODS: The DE019 (methotrexate [MTX]-inadequate responders, longstanding RA) and PREMIER (MTX-naive, early RA) studies, respectively, had 1- or 2-year double-blind periods followed by 9- or 8-year open-label extensions (OLEs). Patients received ADA ± MTX in both OLEs. Radiographic progression was assessed using change from baseline in modified total Sharp score (ΔmTSS). A mixed-effects model was used post hoc to evaluate repeated measurements of different data campaigns and to estimate ΔmTSS through up to 10 years of treatment based on original randomization groups (placebo [PBO] + MTX or standard dose ADA + MTX). RESULTS: Data from patients with baseline and ≥1 postbaseline radiograph were included (n = 327 for DE019; n = 452 for PREMIER). Integrated and 10-year completer ΔmTSS progression curves differed slightly. In DE019, for patients originally assigned PBO + MTX, accrued ΔmTSS at year 10 was 6.6 units (integrated model) and 6.2 units (completers). For patients originally assigned ADA + MTX, accrued ΔmTSS was 0.9 units by integrated analysis and 0.7 units in completers. In PREMIER, for patients originally assigned PBO + MTX, accrued ΔmTSS at year 10 was 11.2 units (integrated analysis) and 11.0 units (completers). For patients originally assigned ADA + MTX, accrued ΔmTSS was 5.1 units (integrated analysis) and 4.0 units (completers). A higher radiographic progression rate was observed in patients who received delayed versus immediate ADA + MTX treatment. CONCLUSIONS: Longitudinal integrated analysis provided robust estimates of radiographic progression that only slightly differed from completers-only scores and confirmed the effects.
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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 | 0.021 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".