Transitioning patients from efalizumab to alternative psoriasis therapies: findings from an open‐label, multicenter, Phase IIIb study
Bibliographic record
Abstract
BACKGROUND: Rebound in psoriasis is, by definition, a rapid worsening of disease following the discontinuation of therapy for psoriasis; it occurs following the abrupt discontinuation of many therapies. To prevent rebound on discontinuation of efalizumab, this study evaluated the effectiveness of transitioning patients to an alternative psoriasis therapy. METHODS: Patients (n = 130) received subcutaneous efalizumab 1 mg/kg/week for 12 weeks. Efalizumab was discontinued at 12 weeks; patients were evaluated for improvement from baseline in the Psoriasis Area and Severity Index (PASI) and a 12-week transition period was begun. Patients who achieved PASI improvement of 75% or more (PASI-75) at week 12 of efalizumab treatment were observed during the transition period and treated only if psoriasis recurred. Patients who did not attain PASI-75 at week 12 of efalizumab treatment were immediately transitioned to an alternative psoriasis therapy at the physician's discretion. All patients were evaluated for signs of rebound following efalizumab discontinuation. RESULTS: Rebound was not observed in any PASI-75 responder (n = 46). Rebound was observed in two of 32 patients who achieved between PASI-50 and PASI-75, and was more common in nonresponders (14/49). Rebound was observed in none of the eight patients treated with cyclosporine and in two of the 12 patients treated with methotrexate during the transition period. CONCLUSIONS: These results suggest that efalizumab-responsive patients are less likely to experience rebound than nonresponders and may not require treatment until disease recurrence following efalizumab discontinuation. Efalizumab nonresponders are at higher risk of developing rebound and thus should be considered for transition to an appropriate psoriasis therapy immediately following efalizumab discontinuation.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".