Patients With Psoriatic Arthritis–Related Enthesitis and Persistence on Tofacitinib Under Real-World Conditions
Bibliographic record
Abstract
Objective Information on the persistence of tofacitinib (TOF) in psoriatic arthritis (PsA) is scarce in real-world conditions. Our objective was to analyze the persistence and safety of TOF under these conditions. Methods This was a single-center retrospective longitudinal observational study of all patients with PsA who received at least 1 dose of TOF. The main focus was on adverse events (AEs) and drug survival. Drug survival was analyzed by Kaplan-Meier curves and persistence explanatory factors by multivariate Cox regression models. The hazard ratio (HR) was used to measure association. Results Seventy-two patients were included, 54 women and 18 men, mean age 51.9 (SD 11.1) years, mean disease duration of 10.4 (SD 6.99) years. TOF was ≥ third line of therapy in > 70% of cases. The median survival was 13.0 (IQR 5.3-29.0) months. One-year retention rate was 52.7% (95% CI 42.4-65.6). TOF survival was not influenced by sex, disease duration, comorbidities, or line of treatment. Younger patients (HR 0.96,P= 0.01) and those with enthesitis (HR 0.37,P= 0.03) showed lower odds of drug discontinuation. The overall rate of AEs was 52.9 (95% CI 38.5-70.6)/100 person-years. Most AEs occurred during the first 6 months of exposure. Conclusion In this real-world study, TOF showed a reasonably good retention rate in a PsA population that was mostly refractory to biologic and oral targeted synthetic disease-modifying antirheumatic drugs. There were no new causes for concern regarding safety. Patients with refractory PsA and enthesitis might be a specific target population for this drug.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".