Bibliographic record
Abstract
The reasons to measure atherosclerosis include 1) risk stratification and prediction; 2) evaluation of patient response to interventions; and 3) identification of novel genetic, cellular and molecular determinants of risk. Atherosclerosis can be quantified non-invasively using the increasingly reliable and precise modalities described in this issue, which include ultrasound and magnetic resonance imaging. While each modality assesses "atherosclerosis", the particular morphological entities captured may reflect different aspects of atherogenesis with different biological determinants. For instance, among carotid ultrasound determinations, intima-media thickness (IMT) may reflect medial hypertrophy from hypertension, while plaque volume and stenosis and calcium deposition may additionally reflect foam cell proliferation, scarring and/or thrombosis. Clarifying the biological and clinical correlates of images may guide the choice of modality for specific applications. In addition, these tools are presently used to assess structures at a single time point. However, using them to follow temporal changes may further enhance their value. In this regard, certain modalities, such as ultrasound assessment of carotid plaque area or volume, may be more sensitive than others, such as assessment of IMT, for detecting temporal changes in atherosclerosis. Combining modalities--and adding new biomarkers of disease--may be necessary to grasp the full complex vascular phenotypic picture--"phenomics"--of both individual subjects and groups of patients. In evaluating new determinants and novel therapies, it will be important to consider the biology and clinical correlates of a specific measured atherosclerosis phenotype in order to select the most appropriate modality.
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 | 0.003 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".