P607 Higher adalimumab serum levels do not increase the risk of adverse events in patients with inflammatory bowel disease
Bibliographic record
Abstract
The relationship between serum adalimumab concentrations and adverse events in patients with inflammatory bowel disease (IBD) is unknown. We aimed to determine whether patients with IBD using adalimumab are at increased risk of adverse events if they have high adalimumab serum levels compared with those with lower adalimumab levels. This was a retrospective study of 133 IBD patients with at least one measurement of serum adalimumab level available. The cohort was divided according to the median adalimumab level of 9.8 μg/ml. The primary outcome was the rate of overall adverse events between the two groups. Secondary outcomes included the rate of infections, dermatologic reactions, injection-site reactions, and other adverse events in both groups. Rates of discontinuation of adalimumab due to adverse events was also evaluated. Multi-variate logistic regression analysis was also performed to evaluate the relationship between adalimumab levels and adverse events. A total of 27 adverse events were reported in 133 patients in the overall cohort. In patients with higher adalimumab levels, there were 17 adverse events reported in a total of 66 patients, which was not significantly different than the 10 adverse events reported in 67 patients with lower adalimumab levels (25.7% vs. 14.9%, p = 0.12). Stratification of patients into adalimumab level tertiles did not show any difference in the rate of adverse events between the three groups. After adjustment for potential covariates, IBD patients with higher adalimumab levels did not have higher odds of an adverse event than patients with lower levels (OR 1.94, 95% CI 0.81–4.64). There does not appear to be an increased risk of adverse events in IBD patients with higher adalimumab levels.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".