PEER simplified lipid guideline 2023 update
Bibliographic record
Abstract
OBJECTIVE: To update the 2015 clinical practice guideline and provide a simplified approach to lipid management in the prevention of cardiovascular disease (CVD) for primary care. METHODS: , a multidisciplinary, pan-Canadian guideline panel was formed. This panel was represented by primary care providers, free from conflicts of interest with industry, and included the patient perspective. A separate scientific evidence team performed evidence reviews on statins, ezetimibe, proprotein convertase subtilisin-kexin type 9 inhibitors, fibrates, bile acid sequestrants, niacin, and omega-3 supplements (docosahexaenoic acid with eicosapentaenoic acid [EPA] or EPA ethyl ester alone [icosapent]), as well as on 11 supplemental questions. Recommendations were finalized by the guideline panel through use of the Grading of Recommendations Assessment, Development and Evaluation methodology. RECOMMENDATIONS: All recommendations are presented in a patient-centred manner designed with the needs of family physicians and other primary care providers in mind. Many recommendations are similar to those published in 2015. Statins remain first-line therapy for both primary and secondary CVD prevention, and the Mediterranean diet and physical activity are recommended to reduce cardiovascular risk (primary and secondary prevention). The guideline panel recommended against using lipoprotein a, apolipoprotein B, or coronary artery calcium levels when assessing cardiovascular risk, and recommended against targeting specific lipid levels. The team also reviewed new evidence pertaining to omega-3 fatty acids (including EPA ethyl ester [icosapent]) and proprotein convertase subtilisin-kexin type 9 inhibitors, and outlined when to engage in informed shared decision making with patients on interventions to lower cardiovascular risk. CONCLUSION: These updated evidence-based guidelines provide a simplified approach to lipid management for the prevention and management of CVD. These guidelines were created by and for primary health care professionals and their 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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.055 | 0.048 |
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".