Therapeutic Drug Monitoring of TNF Antagonists in Inflammatory Bowel Disease.
Bibliographic record
Abstract
Although tumor necrosis factor (TNF)-α antagonists play a critical role in the treatment of moderate-to-severe inflammatory bowel disease (IBD), several factors can impact treatment response. The degree of systemic inflammation, serum albumin concentration, disease type, body mass index, gender, concomitant therapy with immunosuppressive agents, and especially development of antidrug antibodies (ADAs) are key determinants of TNF antagonist pharmacokinetics and clinical outcomes. Therefore, measurement of serum drug and antibody concentrations in patients with IBD who are on TNF antagonists has the potential to guide clinical decision-making, optimize treatment, improve outcomes, and reduce healthcare costs. Multiple strategies to prevent ADA formation exist, including multiple clinical algorithms that employ therapeutic drug monitoring to optimize treatment following a secondary loss of therapeutic response. An individualized approach is needed, however, to identify early predictors of ADA development and other confounders of TNF antagonist therapy.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".