National Trends in Cardiovascular Care and Outcomes
Bibliographic record
Abstract
The Issue Cardiovascular disease (CVD), including stroke, is the leading cause of death globally.Each year, thousands of Canadians develop or die from CVD.As the leading reason for hospital admissions in Canada, it is also a major economic burden on the healthcare system.Previous studies from Western Europe and the United States have identified a steadily declining rate of death from cardiovascular and cerebrovascular diseases (Ford et al. 2007;Levi et al. 2002).However, it is uncertain whether the rate of decline is similar across common cardiovascular conditions such as heart attack, heart failure and stroke.Furthermore, the obesity epidemic has raised concerns that future generations of Canadians might suffer adverse health consequences (many related to CVD) from the rising rates of obesity in society.The increasing cost of treating patients with CVD with new drugs and devices is also putting major strains on provincial healthcare budgets.A study of national trends in cardiovascular care and outcomes could prove invaluable to decision-makers, clinicians and others involved in planning the future delivery of healthcare services in Canada.Accordingly, a group of over 30 clinician researchers from across Canada, known as the Canadian Cardiovascular Outcomes Research Team (CCORT), conducted a series of studies to evaluate recent national trends in cardiovascular care in Canada.The first three studies from this initiative were published recently, with additional studies nearing completion (Jackevicius et al. 2009;Lee et al. 2009;Tu et al. 2009).Further information about these studies (including a PowerPoint slide collection) and CCORT is available at http:// www.ccort.ca/trends.aspx.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".