Association of Mammographic Density and Benign Breast Calcifications Individually or Combined with Hypertension, Diabetes, and Hypercholesterolemia in Women ≥40 Years of Age: A Retrospective Study
Why this work is in the frame
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Bibliographic record
Abstract
Recent evidence has linked certain mammographic characteristics, including breast calcifications (Bcs) and mammographic density (MD), with atherosclerotic cardiovascular disease risk factors in women, but data are limited and inconsistent. We aimed to evaluate the association of MD and/or Bcs with hypertension, diabetes, and hypercholesterolemia in women ≥40 years of age. Through hospital electronic records, we retrospectively identified mammograms of non-pregnant women aged ≥40 years and without breast cancer and retrieved reports and relevant data. MD and Bcs were recorded; risk factor status was diagnosed based on treatment profile and clinical and laboratory data. In total, 1406 women were included. MD was inversely related to hypertension, diabetes, hypercholesterolemia, triglyceride levels, age, and body mass index (BMI) (p value for trend <0.001). Bcs were positively associated with hypertension, diabetes, hypercholesterolemia, age, BMI, and elevated creatinine (p<0.05). Controlling for age and BMI, MD category A (MD-A) was independently associated with hypercholesterolemia; Bcs were independently associated with diabetes. Combining MD-A with Bcs did not increase the odds significantly. Analysis for additive interactions revealed a significant interaction between MD-A and BMI, increasing the odds of hypertension, and a trend for increased odds of diabetes by adding MD-A and/or Bcs to BMI. Decreased MD and presence of Bcs are associated with hypertension, diabetes, and hypercholesterolemia in women ≥40 years of age. MD-A may represent a new obesity index independently associated with hypercholesterolemia and additive to hypertension risk. Bcs are independently associated with diabetes. Combining MD and Bcs did not improve the odds significantly, which may reflect mechanistic differences.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 it