Sustained Remission in Tumor Necrosis Factor Inhibitor–treated Patients with Rheumatoid Arthritis: A Population-based Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To study frequency, possible baseline predictors, timing, and duration of sustained remission [SR; defined as 28-joint Disease Activity Score (DAS28) < 2.6 for at least 6 mos] in patients with established rheumatoid arthritis (RA) treated with different tumor necrosis factor (TNF) inhibitors [etanercept (ETN), infliximab (IFX), adalimumab (ADA)]. In addition, the aim was to compare (head-to-head) the effectiveness of individual drugs in patients receiving their first anti-TNF treatment. METHODS: All anti-TNF-treated patients with RA included in the observational South Swedish Arthritis Group register were eligible. We identified the patients' first SR periods (time between first visit after treatment initiation with DAS28 < 2.6 and subsequent visit with DAS28 ≥ 2.6). Baseline predictors of SR in biologic-naive patients were studied using multivariate regression models. Remission duration and timing of remission start was estimated with Kaplan-Meier curves. RESULTS: Of the 2416 patients included, 382 (15.8%) fulfilled the criteria for SR. Median estimated duration of SR was 5.25 years. Predictors for SR were male sex, low Health Assessment Questionnaire, low DAS28, methotrexate (MTX) treatment, and the calendar year of treatment start. OR for achieving SR within the first 12 months of treatment were 1.86 for ETN (95% CI 1.33-2.61) compared to IFX. HR for 4 years of SR were 1.32 for ETN (95% CI 1.01-1.74) and 1.84 for ADA (95% CI 1.23-2.78), with IFX as the reference drug. CONCLUSION: SR was uncommon in patients with RA treated with anti-TNF in clinical practice. However, patients remained in SR for a substantial period of time. Concomitant MTX treatment predicts remission. ETN and ADA were more likely in reaching SR.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".