Optimized Infliximab Induction Predicts Better Long‐Term Clinical and Biomarker Outcomes Compared to Standard Induction Dosing
Bibliographic record
Abstract
OBJECTIVES: To evaluate the efficacy of standard and optimized infliximab induction dosing in attaining corticosteroid (CS) free clinical remission at week 52 and the effect that post-induction trough levels have on long-term outcome. METHODS: Inflammatory bowel disease (IBD) patients ≤18 years commenced on infliximab between August 1, 2016, and August 1, 2018, from Vancouver, Canada, and Glasgow, Scotland, were included. The Glasgow cohort followed standard induction while the Vancouver cohort undertook induction optimization based on clinical, biomarker, and proactive infliximab trough levels. Baseline characteristics and laboratory values were documented. RESULTS: In total, 140 children were included [median age 14.1 years (interquartile range (IQR) 12.0-16.0)]; 54% male. CS-free clinical remission at week 52 was higher in the optimized group compared to the standard cohort [65/78 (83%) vs. 32/62 (52%), P < 0.001]. Combined CS-free clinical and biomarker remission (CRP < 5 mg/L) was also higher in the optimized compared to the standard cohort [65/78 (83%) vs 25/62 (40%), P < 0.001]. The median post-induction trough level was higher in children who were in CS-free clinical remission at week 52 [3.6 mg/L (1.5-7.1)] vs. those who were not [2.0 mg/L (0.8-4.1), P = 0.04]. The odds of attaining a therapeutic post-induction trough level were almost 4-fold higher in the optimized group than the standard cohort (OR 3.97, 95% CI: 1.89-8.68, P < 0.001). CONCLUSIONS: Standard infliximab induction resulted in less favorable long-term outcomes for pediatric IBD patients. Optimizing induction using clinical, biomarker, and proactive trough levels resulted in higher post-induction trough levels and a greater odds of attaining long-term clinical remission.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".