Analysis of the coronary artery calcium score for identifying coronary artery plaque in patients clinically referred for preventive screening with non-typical symptoms stratified by age and sex
Bibliographic record
Abstract
AIMS: To evaluate the impact of age and sex on the diagnostic performance of the coronary artery calcium (CAC) score to detect any coronary plaque in individuals referred for preventive screening. METHODS AND RESULTS: We analyzed 1372 patients (56 % male, mean age 51 ± 9 years) referred for preventive coronary artery disease (CAD) screening due to a positive family history of premature CAD in a first degree family member that had non cardiac or atypical symptoms or an abnormal exercise tolerance test. All individuals underwent CAC scoring and coronary computed tomography angiography. The diagnostic performance of a CAC score >0 to detect any coronary plaque was assessed across 5-year age categories, stratified by sex. Coronary plaque was demonstrated by CCTA in 761 patients (55 %), including 156 individuals (20 %) with a CAC score of 0. The sensitivity of the CAC score increased with age, from 53 % (95%CI: 34-71 %) in individuals aged <40-93 % (95%CI: 84-98 %) in patients ≥65 years old. Consequently, a CAC score of 0 resulted in an absolute risk reduction for coronary plaque of 8 % in patients <40, whereas this was 50 % in individuals ≥65. The sensitivity of the calcium score in detecting coronary plaque was generally higher in males and patients on lipid lowering therapy across all age groups. CONCLUSIONS: The diagnostic accuracy of a CAC score of 0 improves with age. A CAC score of 0 does not reliably exclude coronary plaque in younger individuals, whereas its utility is greater in older adults.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".