Approach to identifying and managing atherogenic dyslipidemia: a metabolic consequence of obesity and diabetes.
Bibliographic record
Abstract
OBJECTIVE: To review the evidence for recognition and management of atherogenic dyslipidemia. SOURCES OF INFORMATION: High-quality randomized trials and meta-analyses were available to address most questions. North American and European guidelines were reviewed. Of these, the Canadian Cardiovascular Society lipid guidelines were most congruent with current literature. MAIN MESSAGE: Atherogenic dyslipidemia is characterized by low levels of high-density lipoprotein (HDL), high levels of triglycerides, and a high low-density lipoprotein (LDL) particle number. The condition is highly associated with cardiovascular disease (CVD) and is poorly reflected in Framingham risk score and LDL measurements. Obesity, glucose intolerance, diabetes, and metabolic syndrome are rapidly becoming more common, and are often associated with atherogenic dyslipidemia, affecting long-term CVD risk. Recognition in the office is best achieved by non-HDL or total cholesterol-HDL ratio testing. Treatment success lies in optimizing diet and exercise. Of available medications, statins produce the most benefit and can be titrated to patient tolerance rather than to LDL target levels, which have a poor evidence base. The addition of fenofibrate can be considered in patients with high triglyceride and low HDL levels who have responded poorly to or have not tolerated statins. CONCLUSION: Growing obesity prevalence creates a CVD risk that might be missed by LDL cholesterol testing alone. Simple calculations from results of a non-fasting lipid panel produce non-HDL levels and total cholesterol-HDL ratio, both of which are superior for predicting risk in all patients. These metrics should be available in lipid panels.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".