Ten-year trends in stroke admissions and outcomes in Canada
Bibliographic record
Abstract
BACKGROUND: We analyzed a 10-year stroke administrative dataset to examine trends in admissions, mortality, and discharge destination in Canada. METHODS: We conducted an analysis of hospital administrative data from April 1st 2003 to March 31st 2013 from the Canadian Institute of Health Information's Discharge Abstract Database. Ten-year trends for population-based age- and sex-standardized admission rates were calculated. We reviewed 10-year trends in absolute stroke admissions for differences between provinces and age groups. Stroke 30-day in-hospital mortality rates were calculated and adjusted for sex, age, stroke type and comorbidities. We documented changes in discharge location for ischemic and hemorrhagic stroke patients discharged from acute care. RESULTS: The rate of hospital admissions has declined from 140.2 to 117.5 (per 100,000 people). The number of absolute stroke admissions within provinces increased in Alberta and British Columbia (21.7% and 16.2% respectively). The proportion of stroke patients aged 40-69 years old increased by 4.8% (p<0.0001) over the 10 years, whereas the proportion aged over 70 decreased by 4.9% (p<0.0001). Risk-adjusted 30-day in-hospital mortality decreased from: 18.5% to 14.9% for all strokes; 15.2% to 12.1% for ischemic strokes; 35.6% to 29.7% for intracerebral hemorrhage; and 25.1% to 18.0% for subarachnoid hemorrhage. The absolute increase in patients requiring inpatient and outpatient support increased by 4% (p<0.0001). CONCLUSION: The rate of admissions for stroke is decreasing but there is an increase in stroke admissions for younger patients. In-hospital mortality is decreasing; fewer patients are going directly home without services and more are requiring support 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".