Recent advances in pathogenesis, assessment, and treatment of atherosclerosis
Bibliographic record
Abstract
In recent years, there have been a number of advances in the pathogenesis and treatment of atherosclerosis and in assessing prognosis in carotid atherosclerosis. Risk stratification to improve vascular prevention by identifying patients most likely to benefit from intensive therapy is much improved by measuring carotid plaque burden. In patients with asymptomatic carotid stenosis, a number of modalities can be used to identify the 10-15% who could benefit from endarterectomy or stenting. Transcranial Doppler embolus detection, echolucency and ulceration on 3D ultrasound, intraplaque hemorrhage on magnetic resonance imaging (MRI), and reduced cerebrovascular reserve are useful already; new approaches including plaque texture on ultrasound and imaging of plaque inflammation and early calcification on positron emission tomography/computed tomography (PET/CT) are in development. The discovery that the intestinal microbiome produces vasculotoxic metabolites from dietary constituents such as carnitine in meat (particularly red meat) and phosphatidylcholine from egg yolk and other sources has revolutionized nutritional aspects of vascular prevention. Because many of these vasculotoxic metabolites are removed by the kidney, it is particularly important in patients with renal failure to limit their intake of red meat and egg yolk. A new approach to lowering low-density lipoprotein (LDL) cholesterol by blocking the action of an enzyme that destroys LDL receptors promises to revolutionize vascular prevention once less costly treatments are developed, and a new approach to vascular prevention-"treating arteries instead of risk factors"-shows promise but requires randomized trials. These advances all promise to help in the quest to prevent strokes in high-risk patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".