Coronary artery calcification detected on low‐dose computed tomography in high‐risk participants of an Australian lung cancer screening program: A prospective observational study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Coronary artery calcification (CAC) is a frequent additional finding on lung cancer screening (LCS) low-dose computed tomography (LDCT). Cardiovascular disease (CVD) is a major cause of death in LCS participants. We aimed to describe prevalence of incidental CAC detected on LDCT in LCS participants without prior history of coronary artery disease (CAD), evaluate their CVD risk and describe subsequent investigation and management. METHODS: Prospective observational nested cohort study including all participants enrolled at a single Australian site of the International Lung Screen Trial. Baseline LDCTs were reviewed for CAC, and subsequent information collected regarding cardiovascular health. 5-year CVD risk was calculated using the AusCVD risk calculator. RESULTS: 55% (226/408) of participants had CAC on LDCT and no prior history of CAD, including 23% with moderate-severe CAC. Mean age of participants with CAC was 65 years, 68% were male. 53% were currently smoking. Majority were high risk (51%) or intermediate risk (32%) of a cardiovascular event in 5 years. 21% of participants were re-stratified to a higher CVD risk group when CAC detected on LCS was incorporated. Only 10% of participants with CAC received lifestyle advice (only 3% currently smoking received smoking cessation advice). 80% of participants at high-risk did not meet guideline recommendations, with 47% of this group remaining without cholesterol lowering therapy. CONCLUSION: LCS with LDCT offers the potential to identify and communicate CVD risk in this population. This may improve health outcomes for high-risk LCS participants and further personalize management once screening results are known.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".