Clinical and Laboratory Characteristics Are Associated With Biologic Therapy Use in Pediatric Inflammatory Bowel Disease: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Biologic agents are a highly useful class of medications for treating inflammatory bowel disease (IBD). Limited evidence exists to guide initiation of biologic therapy, especially in pediatric patients. It is unclear if disease severity is connected to biologic response. We hypothesized that the clinical, biochemical and radiographic characteristics of pediatric IBD at diagnosis were associated with subsequent initiation of biologic therapy. METHODS: We performed a retrospective analysis of the charts of all pediatric patients diagnosed with IBD at our centre over 14 years. Kaplan-Meier curves evaluated patient characteristics at diagnosis with time to initiation of biologic therapy. A Cox proportional hazards model was used for multivariate characteristic analysis. RESULTS: A total of 198 patients were included, 57.6% had Crohn's disease, 27.8% had ulcerative colitis and 14.6% had IBD type unclassified. Mean follow-up time was 47.8 months. About 55.5% of the patients received a biologic medication, the mean time to biologic initiation was 21.5 months. Earlier initiation of biologic therapy was frequently associated with older age, higher disease activity index and lower serum albumin. CONCLUSIONS: Older pediatric patients with more severely active disease and lower serum albumin levels at the time of IBD diagnosis were more likely to initiate biologic therapy when considering biologic initiation, even many years after diagnosis. Identification of these characteristics may help inform decisions to initiate biologic therapy earlier in the IBD disease course.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".