Temporal trends in 30-day and 90-day hospital readmission rates among individuals with inflammatory bowel diseases in Ontario, Canada: a population-based study
Bibliographic record
Abstract
Background: Individuals with inflammatory bowel diseases (IBD) are at increased risk of repeated disease-related hospital admissions, some of which may be preventable with targeted outpatient interventions. We assessed population-level trends in the rates of IBD-specific hospital readmission within 30 and 90 days of index hospitalization among those with Crohn's disease (CD) and ulcerative colitis (UC) during a period marked by major changes to IBD management. Methods: We accessed Ontario health administrative datasets to study CD (2002-2017) and UC (2004-2020) patients hospitalized for IBD-specific indications. We compared IBD-specific 30-day and 90-day hospital readmission rates across 4 (UC) and 5 (CD) year time periods using multivariable logistic regression, controlling for age, sex, comorbidities, residential setting, household income, hospital type, and clustering of admissions within patients. Results: Among CD patients, 30-day readmission rates decreased from 9.7% to 7.4%, and 90-day rates decreased from 16.0% to 14.1% between 2002-2007 and 2012-2017 periods. There was a higher likelihood of 30-day readmission during 2002-2007 (adjusted odds ratio [aOR] 1.32; 95% CI, 1.16-1.50) and 2007-2012 (aOR 1.15; 95% CI, 1.01-1.32), and of 90-day readmission during 2002-2007 (aOR 1.14; 95% CI, 1.03-1.26), as compared to 2012-2017. Among UC patients, readmission rates remained stable across time periods. Conclusion: Inflammatory bowel disease-related early rehospitalization risk has declined over time among individuals with CD but not among individuals with UC.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".