Long‐term Outcomes of Infliximab Use for Pediatric Crohn Disease
Bibliographic record
Abstract
BACKGROUND: Data on long-term real-world outcomes of infliximab in pediatric Crohn disease are limited. AIM: The aim of the study was to evaluate infliximab optimization and durability in children with Crohn disease. METHODS: We performed a retrospective review of children with Crohn disease who started infliximab from January 2008 to December 2012 in 4 Canadian tertiary care centers. A priori factors associated with optimization and discontinuation from loss of response were evaluated using logistic regression and Cox proportional hazards model, respectively. RESULTS: One hundred eighty children (54.4% boys) started infliximab; all completed induction. Median age at infliximab start was 14.3 years (Q1, Q3: 12.8, 15.9 years) and median time from diagnosis to infliximab start was 1.5 years (Q1, Q3: 0.6, 3.5 years). At last follow-up, 87.1% were maintained on infliximab (median duration follow-up 85.9 weeks [Q1, Q3: 43.8, 138.8 weeks]). Infliximab optimization occurred in 57.3% (dose escalation 15.2%, interval shortening 3.9%, both 38.2%), primarily due to loss of response. Younger age at diagnosis (<10 years old) and nonstricturing, nonpenetrating behavior were associated with optimization (odds ratio 6.5, 95% confidence interval [CI] 2.0-21.1 and odds ratio 2.1, 95% CI 1.0-4.2, respectively). The 1- and 2-year durability of infliximab (percentage in follow-up who were continuing on infliximab) were 95.5% (95% CI 90.4-98.3) and 91.0% (95% CI 82.4-96.3), respectively. Annual discontinuation due to loss of response occurred at 3.2% per year (95% CI 1.1-5.2). CONCLUSIONS: Children with Crohn disease maintain a durable response to infliximab. Optimization occurs frequently and allows for continued use. Younger age at diagnosis and nonstricturing, nonpenetrating behavior are associated with increased need for infliximab 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".