Joint BrEast CAncer & CardiOvascular ScreeniNg: BEACON Study to Assess Opportunistic Cardiovascular Screening Using Breast Arterial Calcification on Mammography
Bibliographic record
Abstract
Purpose: Breast arterial calcifications (BAC) are not routinely reported on mammography but are linked to coronary artery calcification (CAC) and cardiovascular disease (CVD) events. We sought to assess primary care provider (PCP) follow-up after BAC and CAC notification and the association between BAC on mammography and CAC on CT. Methods: Participants without known CVD undergoing mammography at a single centre were prospectively recruited over 18 months. BAC were qualitatively scored (none/mild/moderate/severe) by 2 breast radiologists. All participants had research cardiac CT for CAC within 6 months, scored using the Agatston method. Questionnaires collected baseline demographics, risk factors, and follow-up data. Results: 286 participants were included (median age 62 ± 10). Prevalence of BAC was 13% (38/286), 248 had none, 18 mild, 16 moderate, and 4 severe. For CAC: 180 had none, 70 had mild (CAC 1-99), 28 had moderate (CAC 100-399), and 8 had severe (CAC >400). For detecting CAC, BAC presence had 92% specificity (166/180), 23% sensitivity (24/106), and 67% negative predictive value (166/248). Most participants with BAC and CAC (71%, 17/24) were not on lipid-lowering therapy and 63% (15/24) did not believe they had elevated CVD risk. At follow-up (median 202 days), 46% (11/24) with BAC and CAC implemented lifestyle modifications, 92% (22/24) scheduled PCP follow-up, and 56% (10/18) underwent further CV risk assessment following their appointment. One participant with BAC and CAC had a stroke during follow-up. Conclusion: In a prospective cohort without known CVD undergoing mammography, notification of BAC and CAC status prompted high follow-up rates with PCPs and lifestyle modifications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".