Bibliographic record
Abstract
Vascular prevention is most cost-effective in high-risk patients, but secondary prevention misses many opportunities. The high-risk strategy- identifying patients with high levels of risk factors-is problematic because traditional risk factors predict only half of vascular events. In multiple regression, traditional risk factors explained only half of carotid atherosclerosis. New strategies are being explored, such as electron-beam computerized tomographic measurement of coronary calcification, to identify high-risk patients. Carotid plaque is a powerful tool for identifying and managing high-risk vascular patients, as it explains twice as much of unexplained vascular risk as coronary calcium by electron beam computerized tomography, and it has significant advantages compared with intimal-medial thickness. After adjustment for risk factors, patients in the highest quartile of baseline plaque area have 3.5 times the risk of stroke, death, or myocardial infarction compared with those in the lowest quartile. Those with regression or stable plaque have half the risk of those with progression after adjustment for the same panel of risk factors. The therapeutic target is plaque regression or stabilization, not just control of traditional risk factors. Trying to treat arteries without measuring plaque is like trying to treat hypertension without measuring the pressure, or hyperlipidemia without measuring the lipids.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.042 | 0.019 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".