A28 INCREASED EMERGENCY DEPARTMENT VISITS AND HOSPITALIZATIONS FOR INFECTIOUS DISEASES IN ELDERLY PATIENTS WITH INFLAMMATORY BOWEL DISEASE: A POPULATION-BASED MATCHED COHORT STUDY
Bibliographic record
Abstract
The prevalence of IBD in elderly people ≥65y is growing rapidly in Canada. This group presents unique clinical challenges due to their comorbidity burden and increased susceptibility to infection. To compare the incidence of emergency department (ED) visits and hospitalizations for infectious diseases in people with elderly-onset IBD compared to people without IBD. Incident cases of elderly IBD (≥65y at diagnosis) diagnosed between 2002–2013 were identified from health administrative data in Ontario using a validated algorithm. Cases were age- and sex-matched to five controls. ED visits and hospitalizations for infectious diseases were identified from the National Ambulatory Care Reporting System and the CIHI Discharge Abstract Database, respectively. Negative binomial models were used to compare the incidence of any infection, any gastrointestinal infection, Clostridium difficile, influenza/pneumonia, sepsis, skin infection, or urinary tract infection. Models were adjusted for rural/urban residence, income, and ADG comorbidity index. Results are presented as incidence rate ratios (IRR) and 95% confidence intervals (CI). Cases of IBD, CD, and UC were at increased risk of hospitalization for all types of infections studied (Table). Of all infections analyzed, the greatest increase in the relative incidence compared to controls was seen for C. difficile (IBD: IRR 13.38, 95% CI 9.50–18.84). Patients with IBD, CD, and UC had also increased incidence of infection-related ED visits (Table). The highest relative incidence of ED visit was similarly noted for C. difficile (IBD: IRR 8.95, 95% CI 5.69–14.09). The 5-year risk of hospitalization and ED visits for any serious infection in patients with IBD were 29% and 40%, respectively, compared to 5% and 17% in controls. Elderly patients with IBD are at significantly increased risk of infections requiring acute care and hospitalization. Treatment strategies to minimize infections in elderly patients should be considered. Incidence rate ratio (IRR) and 95% confidence interval (CI) comparing the risk of emergency department (ED) visit and hospitalization for infectious diseases in elderly patients with IBD compared to matched controls. CAG, CCC, CIHR
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".