The effectiveness of leflunomide in psoriatic arthritis.
Bibliographic record
Abstract
OBJECTIVES: This study aimed to evaluate the effectiveness and safety of leflunomide alone and in combination with methotrexate in the treatment of psoriatic arthritis (PsA). METHODS: Patients were followed at the University of Toronto PsA Clinic. PsA patients who received leflunomide alone or in combination with methotrexate were identified from the PsA clinic database. Effectiveness was defined by drug persistence, a ≥40% reduction in actively inflamed joints, a ≥40% reduction in swollen joint count, and PASI50 and PASI75 response following treatment with leflunomide. Descriptive statistics and logistic regression analyses with stepwise selection were used for data analysis. RESULTS: 85 patients were identified. 43 patients (50.6%) were on leflunomide alone and 42 (49.4%) patients were on combined leflunomide and methotrexate therapy. 30 patients discontinued leflunomide mainly due to toxicity. Of the 55 patients who continued the drug, 38%, 48% and 56% achieved a ≥40% reduction of actively inflamed joint count at 3, 6 and 12 months, respectively. PASI50 was achieved by 27%, 28% and 38% at 3, 6 and 12 months, whereas PASI75 was achieved by 19% at 3 and 6 months and 32% at 12 months. Longer duration of PsA and higher swollen joint count at baseline were predictive for improvement of the swollen joint count at 3 months. The use of concomitant MTX was predictive of achieving PASI50 at 12 months. CONCLUSIONS: Leflunomide led to improvement in almost 50% of the patients by 1 year. Those also taking methotrexate were more likely to achieve a PASI50 response.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".