The Cardiovascular Health in Ambulatory Care Research Team (CANHEART)
Bibliographic record
Abstract
BACKGROUND: The CArdiovascular HEalth in Ambulatory care Research Team (CANHEART) is conducting a unique, population-based observational research initiative aimed at measuring and improving cardiovascular health and the quality of ambulatory cardiovascular care provided in Ontario, Canada. A particular focus will be on identifying opportunities to improve the primary and secondary prevention of cardiovascular events in Ontario's diverse multiethnic population. METHODS AND RESULTS: A population-based cohort comprising 9.8 million Ontario adults ≥20 years in 2008 was assembled by linking multiple electronic survey, health administrative, clinical, laboratory, drug, and electronic medical record databases using encoded personal identifiers. The cohort includes ≈9.4 million primary prevention patients and ≈400,000 secondary prevention patients. Follow-up on clinical events is achieved through record linkage to comprehensive hospitalization, emergency department, and vital statistics administrative databases. Profiles of cardiovascular health and preventive care will be developed at the health region level, and the cohort will be used to study the causes of regional variation in the incidence of major cardiovascular events and other important research questions. CONCLUSIONS: Linkage of multiple databases will enable the CANHEART study cohort to serve as a powerful big data resource for scientific research aimed at improving cardiovascular health and health services delivery. Study findings will be shared with clinicians, policy makers, and the public to facilitate population health interventions and quality improvement initiatives.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".