Dyslipidemias and the primary prevention of cardiovascular disease: analysis of the FAMUS primary care register.
Bibliographic record
Abstract
BACKGROUND: Primary prevention of cardiovascular disease with a pharmacological approach to dyslipidemias is controversial. Little is known about the clinical management by general practitioners in this area. OBJECTIVES: To evaluate the patterns of treatment of patients in primary prevention who were entered in the FAmily Medicine, Université de Sherbrooke (FAMUS) register and to calculate the probability of their receiving a hypolipidemic agent according to the presence of various risk profiles. PATIENTS AND METHODS: Descriptive study based on the FAMUS prospective primary care register. Data from patients in primary prevention (those who had not sustained a cardiovascular event) were extracted and analyzed. MAIN RESULTS: Of the 52,505 patients in the register, 48, 190 were identified as being in primary prevention. Of these, 22,250 (46.2%) had a complete lipid profile on record, and 2300 had received a prescription for a hypolipidemic agent (4.8%). Patients under pharmacological treatment had significantly higher lipid values. The adjusted relative risk of being treated with a hypolipidemic agent was 1.3 for smokers, 1.3 for diabetic patients, 2.0 for those with a positive family history of premature cardiovascular disease, 2.2 for hypertensives and 3.3 for men over 45 years of age or women over 55 years, compared with patients who were not taking lipid-lowering medications. The number of risk factors was even more strongly associated with the probability of being treated. CONCLUSION: Overall, few patients in primary prevention in the register were treated with a pharmacological agent. The presence of associated risk factors in this study was an important predictor for treatment, suggesting that patients in primary prevention are being evaluated globally as a function of all of their risk factors, not just their lipid and lipoprotein levels. Further attention, nonetheless, needs to be directed to the segment of the population with multiple risk factors whose lipoprotein profile is unknown or who are not being treated to guideline target levels.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".