Management of dyslipidemia in primary care.
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease remains the leading cause of mortality in Canada. The link between hyperlipidemia and coronary heart disease has been clearly established. There is overwhelming evidence for reductions in coronary events and cardiovascular mortality with lowering of low-density lipoprotein cholesterol (LDL-C). Despite the evidence, hyperlipidemia treatment remains suboptimal. OBJECTIVE: To evaluate compliance with published dyslipidemia guidelines in a primary care setting. The primary outcome measure was target LDL-C level. METHODS: Retrospective chart review of a random selection of 300 patients diagnosed with hyperlipidemia in a large academic family medicine clinic. The primary outcome measure was a target LDL-C level of less than 2.5 mmol/L for patients with diabetes or coronary heart disease. For patients without diabetes or coronary heart disease, Framingham risk assessment tables were used to determine ideal target LDL-C levels. RESULTS: Overall, 53% of patients achieved target LDL-C. Target LDL-C levels were achieved in 48% of patients with diabetes or coronary heart disease. Males were twice as likely to be prescribed lipid lowering therapy than females. Males on lipid lowering therapy were twice as likely as females on lipid lowering therapy to achieve target LDL-C levels. Males with diabetes or coronary heart disease were twice as likely as females with diabetes or coronary heart disease to achieve target LDL-C levels. Only 44% of patients with diabetes or coronary heart disease were prescribed lipid lowering therapy. CONCLUSION: Results from an academic family medicine clinic indicate suboptimal compliance with current dyslipidemia management guidelines. Primary care physicians need to continue to take an aggressive stance on lipid lowering strategies, especially in high-risk patients and females.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".