Plaque burden, microstructures and compositions underachieving very low LDL-C levels
Bibliographic record
Abstract
PURPOSE OF REVIEW: To summarize the impact of lowering LDL-C on plaque progression, microstructures and compositions. RECENT FINDINGS: Low-density lipoprotein cholesterol (LDL-C) is a major therapeutic target to prevent atherosclerotic cardiovascular disease. Intravascular imaging has elucidated antiatherosclerotic effects of lowering LDL-C in vivo. Intensive control of LDL-C with a statin has been shown to slow plaque progression and induce its regression if very low LDL-C level is achieved. This therapeutic approach has been also demonstrated to modulate plaque microstructures and compositions. These mechanistic insights on intravascular imaging support the benefit of lowering LDL-C in achieving better cardiovascular outcomes. SUMMARY: Lowering LDL-C level has become the first-line therapy in the primary and secondary prevention settings. The effects of lowering LDL-C on plaque progression, microstructures and compositions will be reviewed in this article.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".