Association Between Coronary Artery Disease Diagnosed by Coronary Angiography and Breast Arterial Calcifications on Mammography: Meta-Analysis of the Data
Bibliographic record
Abstract
BACKGROUND: Previous studies evaluating breast arterial calcifications (BAC) as a risk marker for coronary artery disease (CAD) have been limited by sample size and have yielded mixed results. Our objective was to evaluate the association of BAC and CAD. METHODS: Data sources included Medline (1970-2010), the Cochrane Controlled Trials Register electronic database (1970-2010), and CINAHL (1970-2010). The search strategy included the keywords, breast artery calcification, vascular calcification on mammogram, coronary angiography, and meta-analysis. Eligible studies included female patients who had undergone coronary angiography, the gold standard for diagnosing CAD, and had screening mammograms that revealed the presence or absence of BAC. Information on eligibility criteria, baseline characteristics, results, and methodologic quality was extracted by two reviewers. Disagreements were resolved by consensus. RESULTS: A total of 927 patients were enrolled in the five studies. There was a 1.59 (95% confidence interval [CI] 1-21-2.09) increased odds of angiographically defined CAD in patients with BAC seen on mammography. CONCLUSIONS: The presence of BAC on mammography appears to increase the risk of having obstructive CAD on coronary angiography; thus, BAC may not be a benign finding.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.024 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".