Old and new risk factors for atherosclerosis and development of treatment recommendations
Bibliographic record
Abstract
1. The development of atherosclerosis, including the role of the classical and new risk factors, is briefly reviewed an emphasis on the links between some of these risk factors and atherogenesis. 2. While genetic factors are doubtlessly important, they contribute (from the population point of view) little to the overall burden of atherosclerosis. 3. Our studies on the relationship between abdominal obesity and metabolic risk factors in two ethnic groups, namely individuals of Cantonese and European background, suggest that different ranges for at least some of the risk factors should be established for specific ethnic groups (e.g. waist circumference for individuals of Chinese and European background). 4. Using the example of differences between Cantonese heterozygotes for familial hypercholesterolaemia living in Vancouver, Canada, and Canton, China, we demonstrate how the environment can modify a genetic predisposition for atherosclerosis. 5. Gender differences are illustrated by a study of serum lipoprotein (a) as a risk factor in men and women. 6. The principles of current Canadian recommendations for the assessment and treatment of atherosclerosis are outlined; they are based on knowledge gained from both basic and clinical research of atherosclerosis.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".