Bibliographic record
Abstract
OBJECTIVE: To review the central role of cholesterol in coronary artery disease (CAD), underscore the need for identifying patients at high risk of CAD, and discuss treatment of dyslipidemias. QUALITY OF EVIDENCE: Current literature (1995-2000) was searched via MEDLINE using the MeSH headings "cholesterol," "risk reduction," and "statins." Recommendations in this paper are based mainly on the results of large randomized controlled trials. Preference was given to more recent articles, clinically relevant articles, and landmark clinical trials. MAIN MESSAGE: Lipid lowering, and specifically low-density lipoprotein lowering, has been repeatedly shown in large clinical trials to improve survival dramatically and reduce cardiac events in both primary and secondary prevention. Identifying those at highest risk for future cardiac events is critical because these patients will benefit most from aggressive modification of risk factors. The definition of high risk has been expanded to include patients with diabetes mellitus and peripheral vascular disease, as well as those with established CAD. A full lipid profile is required for these patients to assess risk and develop a lipid-lowering strategy with proven effectiveness. CONCLUSION: With the advent of powerful, efficacious, and well tolerated cholesterol-modifying therapies, lipid normalization should be a mandate for all physicians caring for patients with established CAD and patients at risk of developing CAD.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.013 |
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".