Hospital admission for stroke or transient ischemic attack among First Nations people with diabetes in Ontario: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: First Nations people have high rates of diabetes mellitus, which is a risk factor for stroke. We studied the rates of hospital admission, processes of care and outcomes of stroke and transient ischemic attack (TIA) in First Nations people in Ontario. METHODS: Using linked administrative databases, we identified annual cohorts of people aged 20-105 years in Ontario with prevalent diabetes between Apr. 1, 1995, and Mar. 31, 2015. We identified Status First Nations people in Ontario from the Indian Register. We compared age- and sex-standardized rates of hospital admission for stroke or TIA, processes of care and case fatality among First Nations versus other people in Ontario with diabetes. RESULTS: Overall, 28 874 people with diabetes (of whom 536 were First Nations people) were admitted to hospital with a stroke or TIA between Apr. 1, 2011, and Mar. 31, 2016. Admission rates for stroke or TIA declined over the study period but were higher among First Nations people than other Ontarians in most years after 2005/06. First Nations people admitted with stroke or TIA were as likely as other Ontarians to undergo neuroimaging within 24 hours (94.6% v. 96.0%), be discharged to inpatient rehabilitation (31.8% v. 34.8%) and receive carotid revascularization (1.4% v. 2.7%), but were less likely to receive thrombolysis (6.3% v. 11.0%). Age- and sex-standardized stroke case fatality was similar in First Nations people and other Ontarians at 7 days (12.0% v. 8.5%), 30 days (19.2% v. 16.0%) and 1 year (33.8% v. 28.1%). INTERPRETATION: Rates of hospital admission for stroke or TIA were higher among First Nations people than other people with diabetes in Ontario. Future work should focus on determining Indigenous-specific determinants of health related to this disparity and implementing appropriate interventions to mitigate the risks and sequelae of stroke in First Nations people.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".