Breast arterial calcification on mammography does not predict coronary artery disease by invasive coronary angiography
Bibliographic record
Abstract
BACKGROUND: The relationship between breast arterial calcification (BAC) and angiographic coronary artery disease (CAD) is uncertain. Some studies have shown a positive association between BAC and angiographically proven CAD, while other studies have shown no association. OBJECTIVE: Examine the association between visually detected BAC on mammography and CAD found on invasive coronary angiography (ICA) in women and compare the frequency of risk factors for CAD between women with normal and abnormal ICA. DESIGN: Retrospective. SETTING: Single tertiary care center. PATIENTS AND METHODS: A review of the radiology databases was performed for female patients who underwent both ICA and mammography within six months of each other. Cases were excluded if there was a history of CAD, such as coronary artery bypass graft or prior percutaneous coronary intervention. MAIN OUTCOME MEASURES: BAC as a predictor of obstructive CAD on ICA. SAMPLE SIZE: 203 Saudi women RESULTS: The association between age at catheterization and ICA was statistically significant ( P=.01). There was no association between BAC and abnormal ICA ( P=.108). Women with abnormal ICA were older than women with a normal ICA ( P=.01). There was a higher frequency of CAD risk factors among the patients with abnormal ICA, except for smoking. In the multiple logistic regression model, ICA was associated with age, a family history of CAD, diabetes mellitus, hypertension and hypercholesterolemia. BAC-positive women were older than BAC-negative women ( P=.0001). BAC was associated with age, diabetes, hypertension, and chronic kidney disease in the multiple logistic regression model. CONCLUSIONS: BAC on mammography did not predict angiographically proven CAD. There was a strong association between BAC and age and many other conventional CAD risk factors. LIMITATIONS: Relatively small sample, single-center retrospective study. CONFLICT OF INTEREST: None.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".