P1200 Increasing rate of hospitalization for inflammatory bowel disease is an age-related effect: A Canadian population-based study
Bibliographic record
Abstract
Abstract Background Inflammatory bowel diseases (IBDs), including Crohn’s Disease (CD) and Ulcerative Colitis (UC), are chronic diseases that pose significant challenges to patients and healthcare systems. To understand trends in the risk of all-cause hospitalization for individuals with CD and UC we explored age, period, and cohort effects in Canada. Methods We utilized repeated cross-sectional survey data from the 2005-2014 Canadian Community Health Survey (CCHS) to identify individuals with self-reported CD or UC. These individuals were linked to their hospitalization records from the Discharge Abstract Database (DAD) and were followed for three years. Cross-classified random-effects two-level models were used to estimate fixed effects for age and its quadratic term (level 1) and random effects for time periods and birth cohorts (level 2), adjusted for sex, on the risk of hospitalization within three years. Results An estimated 84,000 CD and 113,000 UC individuals were eligible for study inclusion. From this, an estimated 30,250 and 39,890 all-cause hospitalizations occurred within three years post-entry into the study for CD and UC individuals, respectively. Broadly, the risk of hospitalization within three-years increased with age and across birth-cohorts, with older cohorts experiencing greater risks of hospitalization. A small, but statistically significant temporal effect was identified for both CD and UC groups. Within birth cohorts, the risk of hospitalization increased across ages for CD, but in individuals with UC, the risk of hospitalization decreased across ages, except for the two oldest birth cohorts. Conclusion Overall, results support the hypothesis that age effects are primarily responsible for fluctuations in the risk of hospitalizations1. Risk of all-cause hospitalization has remained relatively stable from 2005 to 2014 in Canada and differences across time periods may be a consequence of the age-distribution at each time period. As the prevalence of CD and UC continues to rise and the Canadian population continues to be comprised of growing number of older-aged individuals, increasing the allocation of healthcare resources to prevent age-related risks of hospitalizations would be beneficial to reduce hospital burdens. References 1.Buie et al. 2023. Doi: 10.1093/ibd/izad020
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".