Correlation Between Breast Arterial Calcifications and Higher Cardiovascular Risk: Awareness and Attitudes Amongst Canadian Radiologists Who Report Mammography
Bibliographic record
Abstract
Background: Breast arterial calcification (BAC) on mammography correlates with increased cardiovascular risk. Reporting BAC is not standard practice. Our study evaluates the awareness of Canadian radiologists who report mammography of the clinical significance of BAC and their attitudes towards reporting BAC compared to their European and American counterparts. Methods: Following local institutional ethics approval, a 25 question survey (SurveyMonkey) was disseminated to Canadian radiologists via provincial and national society email lists. Responses were collected over 5 weeks (April–June 2022). Results: One hundred and eighty-six complete responses were collected. Sixty percent (112/186) were aware of the association between BAC and cardiovascular risk and 16% (29/186) document its presence in mammogram reports. Thirty five percent (65/186) occasionally document BAC if severe or in a young patient. Four percent (7/186) had local departmental guidelines on BAC reporting and 82% (153/186) agreed there is a need for national BAC reporting guidelines. Fewer Canadian radiologists were aware of the association between BAC and cardiovascular risk compared to European radiologists (60% vs 81%), report the presence of BAC compared to both European (15% vs 62%) and American (15% vs 35%) radiologists, and inform the patient of the presence of BAC compared to European radiologists (1% vs 46%). Conclusion: Canadian radiologists who report mammography were less aware of the association between BAC and cardiovascular risk than their European and American counterparts and were less likely to document the presence of BAC. Given the correlation of BAC with increased cardiovascular event risk, there is increased need for awareness as well as national BAC reporting guidelines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".