Bibliographic record
Abstract
PURPOSE OF REVIEW: To summarize the current evidence concerning the role of HDL-C and HDL-associated parameters in the risk for cardiovascular disease (CVD). RECENT FINDINGS: Numerous population studies have shown that plasma levels of HDL-C are inversely associated with CVD risk; in patient care HDL-C levels are therefore widely implemented in risk estimation models. A number of antiatherogenic properties have been ascribed to the HDL particle, but the hypothesis that HDL is causally related to CVD has been seriously challenged by recent data obtained from both human genetic studies and clinical trials. The final word on HDL-C as a therapeutic target is pending, as a number of clinical endpoint trials specifically focusing on the effect(s) of HDL-C increasing agents are underway. Moreover, recent data show that HDL efflux capacity could hold independent predictive value for CVD events, which clearly highlights the potential need to focus on HDL functionality, rather than on HDL-C levels. SUMMARY: The dogmatic concept that HDL-C levels predict future CVD events is undisputed, but the role of HDL-C as a causal factor in atherosclerosis has been challenged by a number of different types of studies. In recent years, a paradigm shift toward 'HDL functionality' is apparent. Whether or not optimizing these markers of HDL functionality actually does reduce CVD risk requires formal testing in prospective controlled studies.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.014 |
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".