Impact of Tofacitinib on Components of the ACR Response Criteria: Post Hoc Analysis of Phase III and Phase IIIb/IV Trials
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of tofacitinib (TOF) on American College of Rheumatology (ACR) response criteria components in patients with rheumatoid arthritis (RA). METHODS: This post hoc analysis pooled data from RA phase III randomized controlled trials (RCTs) assessing TOF 5 or 10 mg BID, adalimumab (ADA), or placebo, with conventional synthetic disease-modifying antirheumatic drugs, and a phase IIIb/IV RCT assessing TOF 5 mg BID monotherapy, TOF 5 mg BID with methotrexate (MTX), or ADA with MTX. Outcomes included proportions of patients achieving ACR20/50/70 responses and ≥ 20/50/70% improvement rates in ACR components at week 2 and months 1, 3, and 6; and mean percent improvement in ACR components and Clinical or Simplified Disease Activity Index (CDAI or SDAI) low disease activity or remission rates, at month 3, for ACR20/50/70 responders. RESULTS: Across treatment groups, ≥ 20/50/70% improvement rates were numerically higher for most physician- vs patient-reported measures. In phase III RCTs, at earlier timepoints, ≥ 50/70% improvements in patient global assessment of disease activity, pain, and physician global assessment were similar. Among ACR20 responders receiving TOF, mean percent improvements for tender and swollen joint counts were > 70% at month 3. CDAI/SDAI remission was achieved at month 3 by 27.8-45.0% of ACR70 responders receiving TOF. CONCLUSION: Among ACR20 responders treated with TOF, physician-reported components particularly exceeded 20% response improvement. At month 3, disease state generally did not corroborate ACR70 response criteria. Divergences between physician- and patient-reported measures highlight the importance of identifying appropriate patient-reported outcome targets to manage RA symptoms in clinical practice. (ClinicalTrials.gov: NCT00847613/NCT00856544/NCT00853385/NCT02187055).
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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 | 0.069 | 0.062 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.021 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".