Worldwide Dyslipidemia Guidelines
Bibliographic record
Abstract
This review aims to identify common features of guidelines by comparing and summarizing similarities between five guidelines distributed by high-profile cardiovascular societies across the globe. We include guidelines from North America: ACC/AHA (2013 American College of Cardiology/American Heart Association Guideline on the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults) and CCS (2016 Canadian Cardiovascular Society Guidelines for the Management of Dyslipidemia for the Prevention of Cardiovascular Disease in the Adult) and from Europe: PoLA (2016 Polish Lipid Association), ESC/EAS (2016 European Society for Cardiology/European Atherosclerosis Society Guidelines for the Management of Dyslipidemias), and NICE (National Institute for Health and Care Excellence) from the UK. We also include the 2016 Chinese guidelines for the management of dyslipidemia in adults in this comparison. All of these guidelines employ a rigorous review of clinical evidence and emphasize the immense importance of statins in the primary and secondary prevention of atherosclerotic cardiovascular disease. Moreover, they place great emphasis on the dialog between the clinician and the patient regarding treatment and the risks associated with it. Despite the differences in statin intensities, safety concerns, use of risk estimators, or treatment of specific patient subgroups, there are more similarities than differences between the guidelines from both a clinical and practical point of view. Physicians ought to understand both similarities and differences in guideline recommendations to make the right decision regarding statin therapy for individual patients.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.016 |
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".