Identification of Distinct Disease Activity Trajectories in Methotrexate‐Naive Patients With Rheumatoid Arthritis Receiving Tofacitinib Over Twenty‐Four Months
Bibliographic record
Abstract
OBJECTIVE: Tofacitinib is an oral JAK inhibitor for the treatment of rheumatoid arthritis (RA). To better understand tofacitinib treatment responses, we used group-based trajectory modeling to investigate distinct disease activity trajectories and associated baseline variables in patients with active RA. METHODS: This post hoc analysis used data from a phase III study of methotrexate-naive patients receiving tofacitinib 5 mg twice daily. Changes in the 4-variable Disease Activity Score in 28 joints, using the erythrocyte sedimentation rate (DAS28-ESR) from baseline to month 24 were used in group-based trajectory modeling to identify distinct disease activity trajectories. Patient and disease characteristics, changes in radiographic progression and patient-reported outcomes, and safety up to month 24 were compared among trajectory groups. RESULTS: From 346 methotrexate-naive patients, 5 disease trajectory groups, defined by DAS28-ESR scores, were identified, which progressed from high disease activity (HDA) to remission (group 1, n = 28), to low disease activity (LDA) rapidly (group 2, n = 107), to moderate disease activity (group 3, n = 98), to LDA gradually (group 4, n = 46), or remained in HDA (group 5, n = 67), at month 24. At baseline, groups 1 and 2 generally had lower disease activity and more favorable patient-reported outcomes, compared with other groups. Improvements in radiographic progression and patient-reported outcomes over 24 months were generally consistent with DAS28-ESR-predicted disease activity trajectories. Adverse event rates were generally comparable across groups. CONCLUSION: Distinct phenotypic subgroups identified heterogeneity in patients with RA normally analyzed as a single population. Trajectory modeling may enable separation of clinically meaningful subsets of patients with RA, and may help optimize treatment outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".