A59 DECREASING HOSPITALIZATIONS AND SURGERIES IN CHILDREN WITH INFLAMMATORY BOWEL DISEASE: A POPULATION-BASED COHORT STUDY
Bibliographic record
Abstract
Abstract Background Health services use in inflammatory bowel disease (IBD) patients cost the Canadian healthcare system $1.3 billion per year, but recent changes to care in children with IBD may have altered trends in health services use. Characterization of these trends would aid health policy makers plan for the healthcare needs of IBD children. Aims To quantify time trends in IBD health services use in children and all-cause health services use in children with and without IBD using a population-based cohort. Methods Using the Ontario Crohn’s and Colitis Cohort, children <18y with IBD diagnosed between 1994–2012 in Ontario were identified using validated algorithms from health administrative data, and matched on age, sex, rurality, and income to children without IBD. We evaluated trends in the number of IBD-specific and all-cause outpatient visits, emergency department (ED) visits, and hospitalizations using negative binomial regression. Cox proportional hazards regression models were used to describe changes in the hazard of intestinal resection (Crohn’s disease; CD) and colectomy (ulcerative colitis; UC) over time. Results are reported as annual percentage change (with 95%CI) for events within 5 years from the diagnosis/index date. Results IBD-specific hospitalization rates decreased by 2.5% (95%CI 1.8–3.2%) per year, but all-cause hospitalization rates in children without IBD decreased faster (APC, 95%CI: 4.3%, 3.5–5.1%, difference in rates p-value=0.0028). The hazard of intestinal resection for CD decreased by 6.0% (95%CI 4.6–7.3%) per year and the hazard of colectomy for UC decreased by 3.0% (95%CI 0.7–5.2%) per year. IBD-specific outpatient visit rates increased after 2005 by 4.0% (95%CI 3.1–4.9%) per year. Similar trends were not observed in children without IBD. Conclusions Decreasing hazards of intestinal resection and colectomy in children with IBD suggest changes in disease management, including more care being provided on an outpatient basis. Decreased hospitalization rates in IBD were mirrored by similar decreases in non-IBD children, indicating universal care changes. Understanding why these trends are occurring may help us better understand how to provide optimal care to children with IBD. Funding Agencies CIHRCanGIEC
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".