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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".