Benefit-risk assessment of tumour necrosis factor antagonists in the treatment of psoriasis
Bibliographic record
Abstract
BACKGROUND: Safety of tumour necrosis factor (TNF) antagonists is a primary concern for clinicians prescribing them to patients with psoriasis. OBJECTIVES: To determine the benefit-risk balance of TNF antagonists in psoriasis. METHODS: Through integrated analyses of published literature, we calculated the number needed to treat (NNT) for various efficacy measures and the number needed to harm (NNH) for various adverse events for approved dosing regimens of adalimumab, etanercept and infliximab. Integrated analyses that included open-label safety data from TNF-antagonist clinical trials were also conducted. RESULTS: PASI 75 treatment effect data from the literature result in NNT values of 1·6 (95% confidence interval, CI 1·5-1·7) for adalimumab 40 mg every other week; 3·2 (95% CI 2·8-3·7) for etanercept 50 mg weekly or 25 mg twice weekly, and 2·3 (95% CI 2·1-2·5) for etanercept 50 mg twice weekly; and 1·4 (95% CI 1·3-1·5) for infliximab 5 mg kg(-1) dosing. For serious noninfectious, serious infectious and malignant adverse events, point estimates of the NNHs are generally at least two orders of magnitude larger than the NNTs, and the 95% CIs for the NNHs for adalimumab, etanercept and infliximab overlap. Analyses that included open-label data corroborated, with increased exposure to study agents, the low risk of adverse events observed in placebo-controlled periods. CONCLUSIONS: These analyses demonstrated that, during the initial year of treatment, the likelihood of success with anti-TNF therapy for psoriasis was several orders of magnitude greater than the likelihood of serious toxicity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.072 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".