Reporting of coronary artery calcification on chest CT studies in breast cancer patients at high risk of cancer therapy related cardiac events
Bibliographic record
Abstract
BACKGROUND: The identification of coronary artery calcification (CAC) detected coincidentally on chest CT exams could assist in cardiovascular risk assessment but may not be reported consistently on clinical studies. Cardiovascular risk factor stratification is important to predict short term cardiac events during cancer therapy and long term cardiac event free survival in cancer patients. We sought to determine the prevalence of CAC and clinical reporting rates in a cohort of cancer patients at high risk of cancer therapy related cardiac events. METHODS: 408 Breast cancer patients who were referred to a cardiac oncology clinic were screened. Inclusion criteria included having had a CT chest and the absence of known coronary disease. Among those screened 263 patients were included in the study. RESULTS: < 0.05). CONCLUSIONS: CAC was commonly detected on chest CT studies in this observational study of breast cancer patients at high risk of cardiac oncology events. The presence of CAC was often not reported clinically but reporting rates have increased over time. Recent SCCT/STR guidelines recommend reporting the presence of CAC on routine chest CT scans in recognition of the importance of CAC as a predictor of cardiovascular events. Reporting of CAC on chest CTs may help to further risk stratify breast cancer patients and improve cardiovascular outcomes in this vulnerable population.
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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 | 0.008 |
| 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 | 0.000 | 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".