Quantifying coronary artery calcification from a contrast-enhanced cardiac computed tomography angiography study
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Bibliographic record
Abstract
AIMS: We sought to quantify coronary artery calcium (CAC) using a single contrast-enhanced cardiac computed tomography angiography (CCTA) study. CCTA has been successfully used for the assessment of coronary artery stenoses, whereas non-contrast ECG-gated computed tomography (Standard-CAC) is commonly performed to quantify CAC. Thus each scan individually contributes to the total radiation dose. METHODS RESULTS: Patients who underwent both Standard-CAC and CCTA scans were identified. Standard-CAC images were scored using the Agatston method. CCTA scans were scored for CAC (CCTA-CAC), whereby CAC was defined as plaque with attenuation 2 SD above the mean attenuation value of the ascending aorta (HU(aorta)). The correlation between Standard-CAC and CCTA-CAC was determined with the slope used to derive a correction factor for the conversion of CCTA-CAC results to a Standard-CAC Agatston score (AS). To test applicability, the correction factor was assessed in a separate validation cohort of similar demographics. From April 2011 to June 2012, a derivation cohort of 92 patients was identified and analysed. An additional 47 patients were identified for the validation cohort. Correlation between Standard-CAC and CCTA-CAC was excellent (r = 0.96). The slope (y = 2.74 × CCTA-CAC score) derived correction factor from the derivation cohort was used to adjust CCTA-CAC derived scores to an AS (CCTA-CAC(corrected) = 2.74 × CCTA-CAC). The correction factor was applied to the validation cohort CCTA-CAC results with excellent agreement between CCTA-CAC(corrected) and Standard-CAC (kappa = 0.93). CONCLUSIONS: Quantification of CAC from a single contrast-enhanced CCTA scan is feasible and correlates well with Standard-CAC. Larger, multicentre studies are needed to validate the universal applicability of CAC quantified using CCTA.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it