Week 2 Adalimumab Levels Predict Short-term Clinical Remission in Patients With Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND AND GOALS: The role of early proactive therapeutic drug level monitoring for anti-tumor necrosis factor therapies is unclear. We aimed to determine whether a week 2 serum trough level in patients with inflammatory bowel disease (IBD) using adalimumab may predict clinical outcomes. MATERIALS AND METHODS: This was a retrospective study of consecutive IBD patients with a week 2 serum adalimumab level available. Receiver operating characteristic curve analysis was conducted to determine an optimal week 2 threshold level for adalimumab. Patients above the threshold were compared for the primary outcome of week 12 clinical remission (CR) and the secondary outcome of short-term endoscopic healing. Multivariate logistic regression analysis was performed to evaluate the relationship between week 2 adalimumab level and CR. RESULTS: Forty-six patients had a week 2 adalimumab level performed. Receiver operating characteristic curve analysis suggested an optimal adalimumab level of 11.9 mcg/mL based on the area under the curve. Patients with week 2 adalimumab levels >11.9 mcg/mL had higher odds of week 12 CR than those with levels below or equal to this threshold (odds ratio=3.34, 95% confidence interval: 1.01-12.11, P =0.04). Other covariates were not found to have a significant association with the primary outcome. The rate of short-term endoscopic healing was numerically higher in patients with adalimumab week 2 levels above 11.9 mcg/mL; however, was not statistically significant (71.4% vs. 28.5%, P =0.11). CONCLUSIONS: Serum adalimumab levels at week 2 appears to be a predictor of short-term CR. Further research should explore whether patients with a week 2 adalimumab level equal to or below 11.9 mcg/mL benefit from early dose optimization.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".