Identification of distinct disease activity trajectories in patients with psoriatic arthritis receiving tofacitinib: a post hoc analysis of two phase 3 studies
Bibliographic record
Abstract
OBJECTIVE: To capture variations in tofacitinib treatment response in psoriatic arthritis (PsA) by identifying patient groups with distinct disease activity trajectories. METHODS: Data were pooled post hoc from two phase 3 studies (OPAL Broaden, OPAL Beyond) in patients with PsA receiving tofacitinib 5 or 10 mg twice daily (n=225, n=226, respectively). Psoriatic Arthritis Disease Activity Score (PASDAS) to month 6 was used in group-based trajectory modelling to identify distinct treatment response groups based on disease state (very low/low/moderate/high disease activity (VLDA/LDA/MoDA/HDA, respectively)). Baseline characteristics, PASDAS components to month 6 and adverse events (AEs) were assessed. RESULTS: Five trajectory groups were identified for both tofacitinib doses: groups improved from MoDA→VLDA/LDA (group 1); HDA→VLDA (group 2); HDA→MoDA rapidly (group 3) or gradually (group 4) or remained in HDA (group 5). Groups 4/5 generally had significantly higher baseline PsA clinical domain scores than groups 1‒3, except for Psoriasis Area and Severity Index/Nail Psoriasis Severity Index. Baseline Leeds Enthesitis Index/Spondyloarthritis Research Consortium of Canada enthesitis scores and tender joint counts were significantly higher in group 4 vs group 2. PASDAS components generally improved to month 6 in all groups, consistent with modelled trajectories. There were no clear trends in AEs across groups. CONCLUSIONS: In patients with PsA receiving tofacitinib, five distinct trajectory groups were identified with different baseline characteristics and treatment outcomes, but no clear trends in AEs. The tofacitinib 5 and 10 mg twice daily models showed comparable trajectories and baseline characteristics. In patients with HDA, enthesitis and tender joint count may impact timing and/or magnitude of response to tofacitinib. Identifying characteristics that impact treatment response may aid personalised treatment algorithm development. TRIAL REGISTRATION NUMBERS: NCT01877668/NCT01882439.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".