Evolution of lipid management guidelines: evidence might set you free.
Bibliographic record
Abstract
OBJECTIVE: To understand how the new guidelines for management of cardiovascular risk by the American Heart Association and the American College of Cardiology (AHA-ACC) can be interpreted and used in a Canadian setting. SOURCES OF INFORMATION: The AHA-ACC guidelines were reviewed, along with all references. Independent PubMed searches were done to include the addition of other lipid-lowering therapy to statins and the use of medical calculators to enhance patient understanding. MAIN MESSAGE: The new AHA-ACC guidelines are based on the best current evidence related to lipid management. This includes use of 10-year cardiovascular disease (CVD) risk as the treatment threshold in place of low-density lipoprotein cholesterol levels, as well as abandonment of low-density lipoprotein treatment targets. There is increased emphasis on dietary and exercise interventions, with the beginning of an effort to quantify the effect of these interventions. Statins are the main drug intervention, and the addition of other drugs to augment lipid lowering is no longer recommended. For application in Canada, Framingham risk tables are more appropriate for risk assessment than the pooled cohort equations used in the United States. Risk calculators for CVD risk should contain information on cardiovascular age and have the ability to represent risk and alternative interventions graphically in order to improve patient understanding and promote informed decision making. CONCLUSION: Focus on the best evidence in CVD risk can simplify lipid management for both the physician and the patient.
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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 | 0.065 | 0.289 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.017 | 0.012 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".