Quantifying coronary artery calcification from a contrast-enhanced cardiac computed tomography angiography study
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".