A.06 Trends in hospital admission and in-hospital mortality for atrial fibrillation related stroke in Canada
Bibliographic record
Abstract
Background: Atrial fibrillation (AF) is associated with increased risk of ischemic stroke. In Canada, the contemporary burden of AF-related stroke is incompletely characterized. Our objective was to determine temporal trends in hospital admissions and in-hospital mortality for AF-related stroke in Canada from 2007 to 2015. Methods: We conducted a retrospective cohort study using Canadian national administrative data to identify admissions to hospital for stroke with comorbid AF between 2007 and 2015. We analyzed temporal trends in age- and sex-standardized proportion of admissions with comorbid AF and associated in-hospital mortality. Results: There were 222,100 admissions to hospital for ischemic (182,990) or hemorrhagic (39,110) stroke. The age-sex adjusted proportion of ischemic stroke admissions with comorbid AF increased from 16.2% to 20.5% (p for trend = 0.02) between 2007 and 2015, and was stable among hemorrhagic stroke. In-hospital mortality for ischemic stroke with comorbid AF decreased from 21.6% to 15.0% (p for trend = 0.001). Conclusions: Rates of hospital admission for ischemic stroke with comorbid AF have increased, while associated in-hospital mortality has decreased. These results identify AF as an important continued focus for stroke prevention. Our findings provide insight into current trends and highlight the need for continued focus on AF-related stroke.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".