Coronary artery calcium on lung cancer screening-CT: An opportunity to optimize cardiovascular disease risk reduction
Bibliographic record
Abstract
Background: In lung cancer screening trials, mortality from cardiovascular disease occurs at similar rates to lung cancer deaths. Survival following lung cancer screening could be optimized if atherosclerosis prevention was targeted. Thus, we sought to determine whether there was potential for improvement in rates of cardiovascular risk reduction therapy based upon coronary artery calcium and cardiovascular risk assessment. Methods: Clinical lung cancer screening-CT reports, lipid lowering therapy and clinical demographics were retrieved from the electronic medical record in the first consecutive 1486 cases without known coronary artery disease from the Ontario High Risk Lung Cancer Screening Pilot program. Lung cancer screening CT images were reviewed for presence and extent of coronary artery calcium. Results: Coronary artery calcium was detected in 83 % and was reported in 63 %. Lipid lowering was prescribed in 60 % of cases whose coronary artery calcium was reported versus 45 % of cases when coronary artery calcium was unreported (p < 0.001). On multivariable analysis, increased Framingham risk score (OR 2.31 95 % CI 1.73-2.31, p < 0.001) and reported coronary artery calcium (OR 1.53 95 % CI 1.22-1.92, p < 0.001) were associated with lipid lowering therapy. Additional cardiovascular risk lowering could be achieved in 21 % using coronary artery calcium and in 44 % with further consideration of clinical risk. Conclusions: In lung cancer screened patients, cardiovascular risk reduction could be optimized significantly by the opportunistic use of coronary artery calcium and clinical assessment. Appropriate cardiovascular risk reduction could attenuate the high prevalence of cardiovascular deaths in these individuals and improve overall survival.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".