Etanercept for patients with psoriasis who did not respond or who lost their response to adalimumab or infliximab
Bibliographic record
Abstract
BACKGROUND: There is a paucity of data on the use of etanercept in patients who have previously failed a different tumour necrosis factor (TNF) alpha antagonist. OBJECTIVES: To study etanercept in patients who did not achieve a satisfactory response to adalimumab or who lost their response to adalimumab or infliximab and to explore the role of anti-adalimumab and anti-infliximab antibodies in etanercept response. METHODS: Patients with psoriasis who did not achieve a satisfactory response to adalimumab or who lost their response to adalimumab or infliximab were included. All patients received etanercept 50 mg twice a week for 12 weeks followed by 50 mg once a week for 12 more weeks. Anti-infliximab and anti-adalimumab antibodies were measured at baseline. The primary objective was to study the efficacy of etanercept using the proportion of patients who achieved a physician global assessment (PGA) of 0 or 1. RESULTS: A total of 81 patients were included. The proportion of patients who achieved a PGA of 0 or 1 after 24 weeks of etanercept was 20.0% (95% CI 4.8-35.2%) for patients who had an unsatisfactory response to adalimumab, 35.1% (95% CI 19.0-51.3%) and 35.7% (95% CI 7.0-64.4%) for patients who lost their response to adalimumab and infliximab respectively. The proportion of patients who achieved a PGA of 0 or 1 at week 24 was numerically higher for patients who had anti-adalimumab or anti-infliximab antibodies (36.5%) as compared to those without (17.2%; P = 0.08). CONCLUSIONS: Etanercept can be effective in patients with psoriasis who failed a previous TNF alpha antagonist.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".