Approach to the diagnosis and management of lipoprotein disorders
Bibliographic record
Abstract
PURPOSE OF REVIEW: Disorders of lipoprotein metabolism are frequently encountered in clinical practice. Although the severe genetic hyperlipidemias are relatively infrequent, prompt recognition and treatment can prevent complications, such as atherosclerosis and pancreatitis. The secondary dyslipidemias, due to medication or other metabolic disorders (hypothyroidism, renal or hepatic diseases), must be identified and treated. With the growing epidemic of obesity, dyslipidemias are a component of the metabolic syndrome. RECENT FINDINGS: The stratification of cardiovascular risk now includes family history and biomarkers of inflammation, especially high-sensitivity C-reactive protein, which enables sound clinical decision making. Lifelong hypercholesterolemia is strongly associated with increasing risk of atherosclerosis and coronary heart disease death, but the decision to treat pharmacologically depends on the absolute cardiovascular risk over the next 10 years. Clinical trial data support intensive treatment of patients at high cardiovascular risk or for the secondary prevention of recurrent coronary heart disease. The recently published JUPITER trial shows that patients with an elevated C-reactive protein benefit from treatment with a statin (rosuvastatin 20 mg) for primary prevention. SUMMARY: The current guidelines for the prevention of coronary artery disease will continue to focus on the determination of global risk, with intensive treatment aimed at the high-risk group. Family history and high-sensitivity C-reactive protein provide additional risk stratification.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".