Simplified lipid guidelines: Prevention and management of cardiovascular disease in primary care.
Bibliographic record
Abstract
OBJECTIVE: To develop clinical practice guidelines for a simplified approach to primary prevention of cardiovascular disease (CVD), concentrating on CVD risk estimation and lipid management for primary care clinicians and their teams; we sought increased contribution from primary care professionals with little or no conflict of interest and focused on the highest level of evidence available. METHODS: Nine health professionals (4 family physicians, 2 internal medicine specialists, 1 nurse practitioner, 1 registered nurse, and 1 pharmacist) and 1 nonvoting member (pharmacist project manager) comprised the overarching Lipid Pathway Committee (LPC). Member selection was based on profession, practice setting, and location, and members disclosed any actual or potential conflicts of interest. The guideline process was iterative through online posting, detailed evidence review, and telephone and online meetings. The LPC identified 12 priority questions to be addressed. The Evidence Review Group answered these questions. After review of the answers, key recommendations were derived through consensus of the LPC. The guidelines were drafted, refined, and distributed to a group of clinicians (family physicians, other specialists, pharmacists, nurses, and nurse practitioners) and patients for feedback, then refined again and finalized by the LPC. RECOMMENDATIONS: Recommendations are provided on screening and testing, risk assessments, interventions, follow-up, and the role of acetylsalicylic acid in primary prevention. CONCLUSION: These simplified lipid guidelines provide practical recommendations for prevention and treatment of CVD for primary care practitioners. All recommendations are intended to assist with, not dictate, decision making in conjunction with patients.
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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".