Adalimumab: long-term safety in 23 458 patients from global clinical trials in rheumatoid arthritis, juvenile idiopathic arthritis, ankylosing spondylitis, psoriatic arthritis, psoriasis and Crohn's disease
Bibliographic record
Abstract
BACKGROUND: As long-term treatment with antitumour necrosis factor (TNF) drugs becomes accepted practice, the risk assessment requires an understanding of anti-TNF long-term safety. Registry safety data in rheumatoid arthritis (RA) are available, but these patients may not be monitored as closely as patients in a clinical trial. Cross-indication safety reviews of available anti-TNF agents are limited. OBJECTIVE: To analyse the long-term safety of adalimumab treatment. METHODS: This analysis included 23 458 patients exposed to adalimumab in 71 global clinical trials in RA, juvenile idiopathic arthritis, ankylosing spondylitis (AS), psoriatic arthritis, psoriasis (Ps) and Crohn's disease (CD). Events per 100 patient-years were calculated using events reported after the first dose through 70 days after the last dose. Standardised incidence rates for malignancies were calculated using a National Cancer Institute database. Standardised death rates were calculated using WHO data. RESULTS: The most frequently reported serious adverse events across indications were infections with greatest incidence in RA and CD trials. Overall malignancy rates for adalimumab-treated patients were as expected for the general population; the incidence of lymphoma was increased in patients with RA, but within the range expected in RA without anti-TNF therapy; non-melanoma skin cancer incidence was raised in RA, Ps and CD. In all indications, death rates were lower than, or equivalent to, those expected in the general population. CONCLUSIONS: Analysis of adverse events of interest through nearly 12 years of adalimumab exposure in clinical trials across indications demonstrated individual differences in rates by disease populations, no new safety signals and a safety profile consistent with known information about the anti-TNF class.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".