Tofacitinib in Rheumatoid Arthritis: Lack of Early Change in Disease Activity and the Probability of Achieving Low Disease Activity at Month 6
Bibliographic record
Abstract
OBJECTIVE: Optimal targeted treatment in rheumatoid arthritis requires early identification of failure to respond. This post hoc analysis explored the relationship between early disease activity changes and the achievement of low disease activity (LDA) and remission targets with tofacitinib. METHODS: Data were from 2 randomized, double-blind, phase III studies. In the ORAL Start trial, methotrexate (MTX)-naive patients received tofacitinib 5 or 10 mg twice daily, or MTX, for 24 months. In the placebo-controlled ORAL Standard trial, MTX inadequate responder patients received tofacitinib 5 or 10 mg twice daily or adalimumab 40 mg every 2 weeks, with MTX, for 12 months. Probabilities of achieving LDA (using a Clinical Disease Activity Index [CDAI] score ≤10 or the 4-component Disease Activity Score in 28 joints using the erythrocyte sedimentation rate [DAS28-ESR] ≤3.2) at months 6 and 12 were calculated, given failure to achieve threshold improvement from baseline (change in CDAI ≥6 or DAS28-ESR ≥1.2) at month 1 or 3. RESULTS: In ORAL Start, 7.2% and 5.4% of patients receiving tofacitinib 5 and 10 mg twice daily, respectively, failed to show improvement in the CDAI ≥6 at month 3; of those who failed, 3.8% and 28.6%, respectively, achieved month 6 CDAI-defined LDA. In ORAL Standard, 18.8% and 17.5% of patients receiving tofacitinib 5 and 10 mg twice daily, respectively, failed to improve CDAI ≥6 at month 3; of those who failed, 0% and 2.9%, respectively, achieved month 6 CDAI-defined LDA. Findings were similar when considering improvements at month 1 or DAS28-ESR thresholds. CONCLUSION: In patients with an inadequate response to MTX, lack of response to tofacitinib after 1 or 3 months predicted a low probability of achieving LDA at month 6. Lack of an early response may be considered when deciding whether to continue treatment with tofacitinib.
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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 | 0.013 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".