Personalized Intervention Based on Early Detection of Atherosclerosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide and challenges the capacity of health care systems globally. Atherosclerosis is the underlying pathophysiological entity in two-thirds of patients with CVD. When considering that atherosclerosis develops over decades, there is potentially great opportunity for prevention of associated events such as myocardial infarction and stroke. Subclinical atherosclerosis has been identified in its early stages in young individuals; however, there is no consensus on how to prevent progression to symptomatic disease. Given the growing burden of CVD, a paradigm shift is required—moving from late management of atherosclerotic CVD to earlier detection during the subclinical phase with the goal of potential cure or prevention of events. Studies must focus on how precision medicine using imaging and circulating biomarkers may identify atherosclerosis earlier and determine whether such a paradigm shift would lead to overall cost savings for global health. • Early-stage subclinical atherosclerosis can be identified in young individuals, but evidence-based strategies are needed to prevent progression of disease and clinical events. • Precision medicine using imaging and circulating biomarkers could facilitate early identification of atherosclerosis and the development of curative interventions. • A paradigm shift based on these principles could reduce the global burden of CVD with enormous implications for population health.
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.
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) | 0.003 | 0.009 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it