Long‐term outcomes of interruption and retreatment vs. continuous therapy with adalimumab for psoriasis: subanalysis of REVEAL and the open‐label extension study
Bibliographic record
Abstract
BACKGROUND: REVEAL was a 52-week study of adalimumab for moderate to severe psoriasis. At Week 33, adalimumab-treated patients with sustained responses (PASI ≥75 at Weeks 16 and 33) were re-randomized to receive adalimumab or placebo. Subsequently, they could receive adalimumab in an open-label extension (OLE) study. OBJECTIVE: To compare long-term efficacy and safety of adalimumab 40 mg every other week (eow), given as continuous treatment or with one period of interruption followed by retreatment. METHODS: Patients who were re-randomized to adalimumab or placebo at REVEAL Week 33 and received ≥ 1 dose of OLE adalimumab were analysed as the continuous and retreatment groups, respectively, for >2 years of OLE treatment with adalimumab 40 mg eow. LOCF was used for missing efficacy data. RESULTS: At OLE Weeks 0, 12 and 24, PASI 75 response rates were 84%, 84%, 86% with continuous treatment (N = 233) vs. 45%, 71%, 79% with retreatment (N = 227). Thereafter, efficacies were slightly greater for continuous treatment but similar between groups, with PASI 75 response rates at OLE Week 108 of 75% vs. 73% respectively. Retreatment was most effective for patients with ≥ PASI 50 responses when retreatment was initiated. Adverse event rates for retreatment were equal to or lower than those for continuous treatment. CONCLUSIONS: In psoriasis patients with sustained PASI 75 responses to adalimumab, long-term efficacy of retreatment after a ≤ 19-week interruption was similar to efficacy achieved with > 3 years continuous treatment. Adalimumab retreatment provided the best results when initiated before responses had declined below PASI 50.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".