New imaging techniques for diagnosing coronary artery disease
Bibliographic record
Abstract
New tomographic cardiovascular imaging tests, such as intravascular ultrasonography (IVUS), coronary computed tomography (CT) angiography and magnetic resonance imaging (MRI), can be used to assess atherosclerotic plaques for the characterization and early staging of coronary artery disease (CAD). Although IVUS images have very high resolution capable of revealing very early preclinical CAD, it is an invasive technique used clinically only in conjunction with a coronary intervention. Multiple-slice coronary CT angiography, which is noninvasive, shows promise as a diagnostic method for CAD. New 64-slice cardiac CT technology has high accuracy for the detection of lesions obstructing more than 50% of the lumen, with sensitivity, specificity, and positive and negative predictive values all better than 90% in patients without known CAD. Cardiac MRI is also improving accuracy in coronary plaque detection and offers a better opportunity for plaque characterization. With further advances in tomographic imaging of coronary atheromas, the goal will be to detect plaques earlier in the development of CAD and to characterize the plaques most likely to generate a clinical event.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".