Estimated Cost Savings of Increased Use of Intravenous Tissue Plasminogen Activator for Acute Ischemic Stroke in Canada
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Intravenous tissue plasminogen activator (tPA) is an economically worthwhile but underused treatment option for acute ischemic stroke. We sought to identify the extent of tPA use in Canadian medical centers and the potential savings associated with increased use nationally and by province. METHODS: We determined the nationwide annual incidence of ischemic stroke from the Canadian Institute of Health Information. The proportion of all ischemic stroke patients who received tPA was derived from published data. Economic analyses that report the expected annual cost savings of tPA were consulted. The analysis was conducted from the perspective of a universal health care system during 1 year. We estimated cost-savings with incrementally (eg, 2%, 4%, 6%, 8%, 10%, 15%, and 20%) increased use of tPA for acute ischemic stroke nationally and provincially. RESULTS: The current average national tPA utilization is 1.4%. For every increase of 2 percentage points in utilization, $757,204 (Canadian) could possibly be saved annually (95% CI maximum loss of $3,823,992 to a maximum savings of $2,201,252). With a 20% rate, >$7.5 million (Canadian) could be saved nationwide the first year. CONCLUSIONS: We estimate that even small increases in the proportion of all Canadian ischemic stroke patients receiving tPA could result in substantial realized savings for Canada's health care system.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".