Comparison of Five Major Guidelines for Statin Use in Primary Prevention in a Contemporary General Population
Bibliographic record
Abstract
Background: Five major organizations recently published guidelines for using statins to prevent atherosclerotic cardiovascular disease (ASCVD): in 2013, the American College of Cardiology/American Heart Association (ACC/AHA); in 2014, the United Kingdom's National Institute for Health and Care Excellence (NICE); and in 2016, the Canadian Cardiovascular Society (CCS), the U.S. Preventive Services Task Force (USPSTF), and the European Society of Cardiology/European Atherosclerosis Society (ESC/EAS). Objective: To compare the utility of these guidelines for primary prevention of ASCVD. Design: Observational study of actual ASCVD events during 10 years, followed by a modeling study to estimate the effectiveness of different guidelines. Setting: The Copenhagen General Population Study. Participants: 45 750 Danish persons aged 40 to 75 years who did not use statins and did not have ASCVD at baseline. Measurements: The number of participants eligible to use statins according to each guideline and the estimated number of ASCVD events that statins could have prevented. Results: The percentage of participants eligible for statins was 44% by the CCS guideline, 42% by ACC/AHA, 40% by NICE, 31% by USPSTF, and 15% by ESC/EAS. The estimated percentage of ASCVD events that could have been prevented by using statins for 10 years was 34% for CCS, 34% for ACC/AHA, 32% for NICE, 27% for USPSTF, and 13% for ESC/EAS. Limitation: This study was limited to primary prevention in white Europeans. Conclusion: Guidelines recommending that more persons use statins for primary prevention of ASCVD should prevent more events than guidelines recommending use by fewer persons. Primary Funding Source: Copenhagen University Hospital.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".