Systematic review of efficacy of anti–tumor necrosis factor (TNF) therapy in patients with psoriasis previously treated with a different anti–TNF agent
Bibliographic record
Abstract
BACKGROUND: Tumor necrosis factor (TNF) antagonists have improved outcomes for patients with psoriasis, but some patients are unresponsive to treatment (primary failure) or lose an initially effective response (secondary failure). OBJECTIVE: We sought to systematically investigate the efficacy and safety of a second TNF antagonist after failure of a first TNF antagonist. METHODS: Published primary studies evaluating the efficacy of switching TNF antagonists after failure were systematically extracted. RESULTS: Fifteen studies were included. Although response rates to a second TNF antagonist were lower than for a first, a substantial proportion of patients in every study achieved treatment success. Week-24 response rates for a second antagonist were 30% to 74% for a 75% improvement in Psoriasis Area and Severity Index score and 20% to 70% for achieving a Physician Global Assessment score of 0/1; mean improvements in Dermatology Life Quality Index ranged from -3.5 to -13. In general, patients who experienced secondary failure achieved better responses than patients with primary failure. Adverse event incidences ranged from 20% to 71%, without unexpected adverse events; 0% to 11% of patients experienced serious adverse events. LIMITATIONS: There was no common definition of treatment failure across these studies of varied design. CONCLUSIONS: Some patients benefit from switching to a second TNF antagonist after failure of a first TNF antagonist, with improved quality of life.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".