Computed tomography quantification of coronary plaque volume may provide further perspective on intermediate severity stenoses.
Bibliographic record
Abstract
Coronary computed tomography angiography (CCTA) is an emerging modality for comprehensive non-invasive assessment of coronary artery disease (CAD). CCTA was traditionally used for anatomical assessment of coronary plaque, including luminal narrowing, plaque burden, location, and composition. Preliminary studies have also demonstrated CCTA's capabilities for functional assessment of coronary plaque, including fractional flow reserve (FFR) and myocardial perfusion-albeit they are not routinely available at all centers and are focus of research. Although the identification and development of treatment strategies of severely stenotic lesions has advanced tremendously over the past years, the evaluation, prognostication, and treatment of patients with intermediate severity stenosis in whom there is equipoise between invasive versus medical management is only now receiving attention. Intermediate severity stenosis is the most likely to benefit from additional measures of disease beyond traditional clinical risk profiling and CCTA visual examination. Nakazato et al. studied 58 patients with intermediate severity stenosis and quantified the percent aggregate plaque volume (%APV), a novel measure of total arterial plaque disease. %APV had the highest correlation with ischemic lesions on FFR, outperforming luminal diameter, luminal area, minimal lumen diameter, and minimal lumen area. This study extracts additional information from pre-existing CT data-sets and suggest novel concept that might improve classification of moderate severity coronary stenoses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".