Effectiveness and efficiency of different guidelines on statin treatment for preventing deaths from coronary heart disease: modelling study
Bibliographic record
Abstract
OBJECTIVE: To examine the potential effectiveness and efficiency of different guidelines for statin treatment to reduce deaths from coronary heart disease in the Canadian population. DESIGN: Modelled outcomes of screening and treatment recommendations of six national or international guidelines--from Canada, Australia, New Zealand, the United States, joint British societies, and European societies. SETTING: Canada. DATA SOURCES: Details for 6760 men and women aged 20-74 years from the Canadian Heart Health Survey (weighted sample of 12,300,000 people) that included physical measurements including a lipid profile. MAIN OUTCOME MEASURES: The number of people recommended for treatment with statins, the potential number of deaths from coronary heart disease avoided, and the number needed to treat to avoid one coronary heart disease death with five years of statin treatment if the recommendations from each guideline were fully implemented. RESULTS: When applied to the Canadian population, the Australian and British guidelines were the most effective, potentially avoiding the most deaths over five years (> 15,000 deaths). The New Zealand guideline was the most efficient, potentially avoiding almost as many deaths (14,700) while recommending treatment to the fewest number of people (12.9% of people v 17.3% with the Australian and British guidelines). If their "optional" recommendations are included, the US guidelines recommended treating about twice as many people as the New Zealand guidelines (24.5% of the population, an additional 1.4 million people) with almost no increase in the number of deaths avoided. CONCLUSIONS: By focusing recommendations on people with the highest risk of coronary heart disease, the Canadian, US, and European societies guidelines could improve either their effectiveness (in terms of hundreds of avoided deaths) or efficiency (in terms of thousands of fewer people recommended treatment) in the Canadian population.
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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 | 0.016 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".