Incremental Prognostic Value of Quantified Vulnerable Plaque by Cardiac Computed Tomography
Bibliographic record
Abstract
BACKGROUND: Coronary computed tomography (CT) angiography (CCTA) has the ability to detect, characterize, and quantify atherosclerotic plaques. The aim of our study was to evaluate the prognostic power of CCTA-quantified plaque subtypes. MATERIALS AND METHODS: A total of 36 patients with adverse events and 36 Morise score-matched patients who remained event free on follow-up were identified. Using CCTA images, plaque subtype volumes in the major epicardial arteries were analyzed using predetermined attenuation ranges in Hounsfield units (HU): 1 to 30 HU (low attenuating), 31 to 70 HU (intermediate attenuating), 71 to 150 HU (high attenuating), and mean coronary lumen+2 SD to 1000 HU (calcified). Each epicardial artery was divided into proximal, mid, and distal segments, and plaque volumes were normalized for arterial segment length. RESULTS: The baseline characteristics of the 2 cohorts were similar. Low-attenuation and intermediate-attenuation plaque volumes were greater in the proximal segments as well as in the entire length in the adverse event compared with the event-free group. High-attenuation plaque volume was increased only in the proximal segments in the adverse event group. There was no difference in the volume of calcified plaque between the 2 groups. The log rank test using a cutoff of 3.99 mm/mm for combined intermediate and low plaque volume showed more adverse events in patients with a plaque volume of ≥3.99 mm/mm. CONCLUSIONS: Adverse events appear to be associated with greater volumes of low-attenuation and intermediate-attenuation plaques that reflect lipid and fibrous atherosclerosis. The difference between the 2 groups is most apparent in the proximal epicardial arteries.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".