Bibliographic record
Abstract
troke leads to significant long-term disability with important ongoing human and economic costs.Although the clinical and economic benefits of thrombolysis have been demonstrated in previous analyses, 1 efficacious large volume stroke care requires systematic organization and commitment on the part of healthcare payers.Canada benefits from a universal single-payer system which faces increasing cost pressures attributable to an ageing population and increasing costs of healthcare technology.The Organization for Economic Cooperation and Development (OECD) estimates that healthcare spending could double as a proportion of GDP in this country by 2050. 2 The Canadian Institutes of Health Information note that Canadian jurisdictions spent 38.7% of all expenditures on health care in 2005 to 2006. 3 Development and maintenance of stroke care systems will require well executed economic analyses to influence policy makers.In this regard, stroke is an ideal condition to treat from an economic perspective because its incidence is strongly agelinked and any intervention that reduces disability is likely to substantially reduce long-term costs.However, any treatment that reduces mortality but leaves disability may increase total costs attributable to the high cost of long-term nursing care of brain-injured patients.Stroke thrombolysis has not been shown to reduce mortality.In the pivotal National Institute of Neurological Disorders and Stroke tissue plasminogen activator (NINDS tPA) Stroke Trial, a nonsignificant 4% reduction in mortality was observed, but this was not confirmed in the pooled analysis of randomized controlled trials.However, thrombolysis does reduce morbidity.Previous cost-effectiveness analyses have suggested large cost-savings per patient treated even with a relatively short 1-year timeline.In the current issue of Stroke, 4 Yip and Demaerschalk use a simple conservative cost-utility model and estimate the potential cost-savings at a Canadian national level from the expanded use of thrombolysis.The calculations are simplified and easy to follow.Using currently available estimates, the cost savings could increase from half a million to $7.5 million annually if 20% of ischemic 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 | 0.004 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.041 | 0.023 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".