Progression to Anti‐TNF Treatment in Very Early Onset Inflammatory Bowel Disease Patients
Bibliographic record
Abstract
OBJECTIVES: Limited data are currently available regarding anti-tumor necrosis factor (TNF) use and outcomes in very early onset inflammatory bowel disease (VEOIBD) patients. We aimed to assess the long-term outcomes and time to progression to anti-TNF treatment in VEOIBD patients. METHODS: We retrospectively reviewed IBD patients diagnosed under 6 years of age, between January 2005 and December 2019, from the British-Columbia (BC) Pediatric IBD database. Demographic data, disease characteristics, disease location and severity were documented. Data on anti-TNF treatment at initiation and during follow up including type of biologic, dosing, and response were collected. Kaplan-Meier curves were used to assess the number of years to progression to anti-TNF treatment and the parameters influencing commencement. RESULTS: Eighty-nine patients with VEOIBD were diagnosed during the study period. Median age at diagnosis was 3.8 years [interquartile range (IQR) 2.6-5.1], 45.3% had Crohn disease (CD) and 62.8% were males. Median duration of follow up was 6.39 years (IQR 3.71-10.55). Anti-TNF treatment was started on 39.5% of patients and 7.0% underwent surgery. Rapid progression to biologic treatment was associated with Perianal fistulizing disease or stricturing disease in CD patients ( P = 0.026, P = 0.033, respectively), and disease severity ( P = 0.017) in ulcerative colitis(UC) patients. The median dose of infliximab at 1 year was 10 mg/kg (IQR 7.5-11) and a median dose interval of 4.5 weeks (IQR 4-6). Clinical remission was reported in 61.8% of patients on their first biologic agent. CONCLUSIONS: The response rate was higher than previously reported and might be due to higher infliximab dosing with shorter infusion intervals than standard dosing.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".