Updated guidelines for the management of dyslipidemia and prevention of cardiovascular disease by pharmacists
Bibliographic record
Abstract
Cardiovascular (CV) disease continues to be a significant cause of morbidity and mortality—coronary disease and cerebrovascular disease are the second and third leading causes of death, respectively, in Canada.1 In 2007, a set of dyslipidemia guidelines tailored to pharmacists based on the 2006 Canadian Cardiovascular Society (CCS) recommendations was published in the Canadian Pharmacists Journal.2 Since then, pharmacists in many provinces have expanded their scope of practice, including in some cases the ability to independently prescribe or modify existing therapies and order laboratory tests. There is growing evidence that pharmacist intervention in the management of dyslipidemia leads to improvements in lipid management.3 Moreover, expanding evidence in the realm of dyslipidemia, including trials in previously understudied populations (chronic kidney disease), innovations in CV risk communication (Cardiovascular Age) and increased awareness of the adverse effect profile of statins led to the publication of the 2012 update of the CCS guidelines for the diagnosis and treatment of dyslipidemia for the prevention of cardiovascular disease in the adult.4 In this article, we provide an update based on the current CCS guidelines, with practical tips for pharmacists.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".