Impact Of Immunomodulator And Biologic Medical Therapies On Need For Intestinal Resection Surgery In Children With Crohn's Disease
Bibliographic record
Abstract
Abstract : Background: Treatment for inflammatory bowel disease has recently changed to include the use of immunomodulators and biologics to induce remission, yet, the affect of their early initiation on pediatrics’ Crohn’s disease course and surgical resection incidence is unknown. Methods: In this single-center retrospective comparative study, the medical charts of patients at the Alberta Children's Hospital diagnosed with Crohn’s disease from January 1, 1983 to January 1, 2012 and followed for a minimum of 1 year were reviewed. Results: There were 122 patients enrolled for a total of 380.4 person-years. From 1983 to 2012, patients with early immunomodulator use increased from 0.0% to 61.5% while maintaining stable 1 and 3 year intestinal resection incidence. Patients (n=53) with a minimum of 3 years follow up and early immunomodulator or biologics use had increased intestinal surgery incidence (100.0% vs 37.2%, P=0.0002) and increased intestinal resection incidence (60.0% vs 27.9%, P=0.03). Conclusions: Immunomodulators and biologics are now initiated more frequently and earlier in treating Crohn’s disease. Intestinal surgery and resection incidence of children with Crohn’s disease has remained stable for the last 30 years. However, intestinal surgery and resection incidence has increased in the subgroup of pediatric patients with early immunomodulator and biologics use.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".