Treatment‐to‐Target With Apremilast in Psoriatic Arthritis: The Probability of Achieving Targets and Comprehensive Control of Disease Manifestations
Bibliographic record
Abstract
OBJECTIVE: The present study was undertaken to evaluate the probability of achieving the Clinical Disease Activity Index for Psoriatic Arthritis (cDAPSA) treatment targets of remission or low disease activity (LDA) with apremilast based on disease activity categories and corresponding responses in arthritis and other domains of psoriatic arthritis (PsA) not included in the cDAPSA. METHODS: Pooled analyses from the Psoriatic Arthritis Long-term Assessment of Clinical Efficacy studies 1, 2, and 3 were performed. Probability analyses assessing the likelihood of achieving cDAPSA treatment targets by week 52 were performed using multiple imputation for discontinuations and missing values. Longitudinal analyses were performed in patients grouped by cDAPSA category at week 52. RESULTS: Among 494 patients in the probability analyses, 46.9% with moderate disease activity and 24.9% with high disease activity at baseline achieved treatment targets (remission or LDA) by week 52. For patients with moderate disease activity at baseline, small improvements (cDAPSA reductions ≥30%) by week 16 were associated with achieving targets. Patients achieving remission or LDA by week 16 had high probabilities of remaining at treatment targets at week 52. Of 375 patients with cDAPSA components available at week 52, achieving targets with apremilast was associated with continuous disease activity improvements and no or mild arthritis and other PsA manifestations. CONCLUSION: The probability of achieving treatment targets (remission or LDA) at week 52 was greater for patients with moderate versus high disease activity at baseline. At a mean level, partial improvements by week 16 were associated with achieving treatment targets. Patients receiving apremilast who achieved cDAPSA targets by week 52 also had no or mild arthritis or other PsA manifestations.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".