Prognostic importance of extensive coronary calcium on lung cancer screening chest computed tomography
Bibliographic record
Abstract
BACKGROUND: Low-dose chest computed tomography (CT) is used for lung cancer screening, but can also detect coronary artery disease as coronary artery calcium. We sought to determine the prevalence and prognostic utility of coronary artery calcium in a population at high risk of cancer. METHODS: We reviewed CT scans from consecutive participants screened for lung cancer between March 2017 and November 2018 as part of the Ontario Health Lung Cancer Screening Pilot for People at High Risk. We quantified coronary artery calcium using an estimated Agatston score. We identified the composite primary outcome of all-cause death and cardiovascular events using linked electronic medical record data from The Ottawa Hospital to December 2023. RESULTS: Among 1486 people who underwent screening CT, coronary artery calcium was detected in 1232 (82.9%) and was extensive in 439 (29.5%). On multivariable analysis, extensive coronary artery calcium was associated with the composite primary outcome (hazard ratio [HR] 2.13, 95% confidence interval [CI] 1.35-3.38), all-cause death (HR 2.39, 95% CI 1.34-4.27), and cardiovascular events (HR 2.06, 95% CI 1.13-3.77). Extensive coronary artery calcium remained predictive of cardiovascular events after we adjusted for noncardiovascular death as a competing risk (HR 2.05, 95% CI 1.09-3.85). INTERPRETATION: Among people undergoing low-dose chest CT for lung cancer screening, extensive coronary artery calcium was an independent predictor of all-cause death and cardiovascular events, even after adjustment for noncardiovascular death. The opportunity to identify and reduce risks from coronary artery disease may represent an additional benefit of lung cancer screening.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".