The Role of HDL-C in the Management of Atherosclerosis
Bibliographic record
Abstract
following Expert Roundtable Discussion was held on November 13, 2011. Dr. Daniel J. Rader from the University of Pennsylvania moderated the topic The Role of HDL-C in the Management of with Drs. H. Bryan Brewer from MedStar Research Institute and Atherosclerosis Research, Jean-Claude Tardif from the Research Center of the Montreal Heart Institute, and Peter P. Toth from the University of Illinois. discussion focused primarily on: (1) epidemiologic association of HDL with coronary disease: causality versus association; (2) HDL metabolism; (3) niacin and the results of the AIM-HIGH trial; (4) additional trials that looked at niacin in raising HDL; (5) the role of fibrates in the management of low HDL; (6) new therapies in development for lowering HDL; (7) lifestyle changes; and (8) the role of CETP inhibitors and modulators. (Med Roundtable Cardiovasc Ed. 2012;3(1):27–37) ©2012 FoxP2 Media, LLC
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".