Quantification of plaque characteristics detected by dual source computed tomography angiography to predict myocardial ischemia as assessed by single photon emission computed tomography myocardial perfusion imaging
Bibliographic record
Abstract
BACKGROUND: We aim to evaluate the relationship between quantitative plaque characteristics detected by dual-source computed tomography angiography (DSCTA) and myocardial ischemia as assessed by single photon emission computed tomography myocardial perfusion imaging (SPECT-MPI). METHODS: In this study, 460 consecutive patients with suspected coronary artery disease (CAD) underwent DSCTA and stress/rest SPECT-MPI, and 179 patients with coronary artery plaques were quantitatively analyzed. Quantitative coronary artery plaque measurements including total plaque volume, the volume of non-calcified plaque, calcified plaque volume, low-density noncalcified plaque volume, total plaque burden, calcified plaque burden, non-calcified plaque burden, low-density non-calcified plaque (LDNCP) burden, remodeling index, plaque length, maximum diameter stenosis were provided by the automated software (Release 5.6.5, Circle Cardiovascular Imaging, Canada). Univariate and multivariate logistic regression analysis was performed to assess the correlation between quantitative plaque characteristics and myocardial ischemia to determine if plaque characteristics were independent of clinical risk factors and significant CAD. RESULTS: 12.9% (6.5-18.5), P=0.011]. In a multivariate analysis, low-density noncalcified plaque burden (OR 1.33; 95% CI, 1.16-1.53, P<0.001) remained a significant predictor of myocardial ischemia after adjusting for stenosis ≥50% and gender. The area under curve (AUC) of the model containing LDNCP burden, stenosis ≥50% and gender was 0.875 (95% CI, 0.812-0.938), which was significantly better than the model with stenosis ≥50% and gender (AUC 0.729; 95% CI, 0.633-0.825). CONCLUSIONS: Quantitative plaque characteristics detected by DSCTA are independently correlated with the incidence of myocardial ischemia by SPECT-MPI in patients with suspected CAD.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".