A63 HOSPITALIZATION RATES FOR INFLAMMATORY BOWEL DISEASE VARY GEOGRAPHICALLY IN SOUTHERN ALBERTA: A POPULATION-BASED COHORT STUDY
Bibliographic record
Abstract
Abstract Background Hospitalization rates for patients with inflammatory bowel disease (IBD) are decreasing throughout Canada; however, this may vary across Canadian jurisdictions. Access to gastroenterologists is limited in many parts of Canada, resulting in care by non-gastroenterologists, and variation in outcomes. Aims To assess trends of hospitalization rates in three regions in Southern Alberta: Calgary zone, a metropolitan city; Chinook region: local gastroenterologists; and Palliser region: no local gastroenterologists. Methods The Alberta IBD Surveillance Cohort is a population-based database consisting of an algorithmically defined prevalent IBD population for Alberta. IBD patients in Southern Alberta were identified by 3-digit postal code and their hospitalizations from the Discharge Administrative Database were extracted (2002 to 2015). IBD patients were stratified by the number of IBD prevalent patients: Calgary Zone (n=9625 in 2015), Palliser region (n=1419), and Chinook region (n=727). Age- and sex- standardized hospitalization rates, per 100 prevalent IBD patients, were calculated for each year. Average Annual Percentage Change (AAPC with associated 95% confidence intervals (CI)) were calculated using the log-linear regression. Rate ratios of standardized hospitalization rates between Calgary, Chinook, and Palliser were calculated. Results From 2002 to 2015 the average hospitalization rate (per 100 prevalent population) was: 27.6 in Calgary, 30.2 in Chinook, and 37.4 in Palliser (Table 1). The AAPCs across these regions were significantly decreasing (Figure 1). By 2011–2015 hospitalization rates fell to 23, 26.3, and 30.2 in Calgary, Chinook, and Palliser, respectively (Table 1). Calgary and Chinook had significantly lower hospitalization rates compared to Palliser (Calgary: 0.72, 95% CI: 0.70, 0.75; Chinook: 0.80, 95% CI: 0.76, 0.84) (Table 1). Conclusions Hospitalization rates for patients with IBD are decreasing, which may be explained by advances in therapeutic modalities and increased expertise of gastroenterologists. The lack of access to a local gastroenterologist in Palliser may account for higher hospitalization rates for patients with IBD. Future studies are needed. Funding Agencies CIHRDHSCN (Digestive Health Strategic Clinical Network), AHS (Alberta Health Services)
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".