Bibliographic record
Abstract
PURPOSE OF REVIEW: Atherosclerotic cardiovascular disease (ASCVD) remains the leading cause of morbidity and mortality in most countries. Modification of common risk factors such as dyslipidaemia can result in significant reduction of ASCVD incidence in the population and improve clinical outcomes. The purpose of this review is to discuss and compare the latest worldwide lipid guidelines, and to demonstrate the variation in practice in different parts of the world. RECENT FINDINGS: The lipid guidelines have recently been updated in different countries. The National Institute for Health and Care Excellence (NICE) guidelines in the United Kingdom were issued in July 2014, are risk based and are broadly similar to the American College of Cardiology/American Heart Association task force guidelines that were published in November 2013. Both these guidelines are in variance with both the Canadian Guidelines and the European Society of Cardiology/European Atherosclerosis Society guidelines 2011, which are target based and have different risk scoring systems, which results in significant variation in practice and increased healthcare costs in certain countries. SUMMARY: The difference in guidelines in different countries makes it difficult for the clinician to standardize the treatment provided to individuals. The variance in risk scoring systems makes it difficult to compare risk prediction tools across countries and hence the optimum treatment available for a given population. Standardization of guidelines based on randomized controlled trial data and validation and calibration of various risk scoring systems could help improve clinical outcomes in this high-risk group of individuals at risk of ASCVD within individual countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.017 |
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".