Cost Avoidance Associated With Optimal Stroke Care in Canada
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Evidence-based stroke care has been shown to improve patient outcomes and may reduce health system costs. Cost savings, however, are poorly quantified. This study assesses 4 aspects of stroke management (rapid assessment and treatment services, thrombolytic therapy, organized stroke units, and early home-supported discharge) and estimates the potential for cost avoidance in Canada if these services were provided in a comprehensive fashion. METHODS: Several independent data sources, including the Canadian Institute of Health Information Discharge Abstract Database, the 2008-2009 National Stroke Audit, and the Acute Cerebrovascular Syndrome Registry in the province of British Columbia, were used to assess the current status of stroke care in Canada. Evidence from the literature was used to estimate the effect of providing optimal stroke care on rates of acute care hospitalization, length of stay in hospital, discharge disposition (including death), changes in quality of life, and costs avoided. RESULTS: Comprehensive and optimal stroke care in Canada would decrease the number of annual hospital episodes by 1062 (3.3%), the number of acute care days by 166 000 (25.9%), and the number of residential care days by 573 000 (12.8%). The number of deaths in the hospital would be reduced by 1061 (14.9%). Total avoidance of costs was estimated at $682 million annually ($307.4 million in direct costs, $374.3 million in indirect costs). CONCLUSIONS: The costs of stroke care in Canada can be substantially reduced, at the same time as improving patient outcomes, with the greater use of known effective treatment modalities.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".