Association between vitamin D and calcium intakes, breast microcalcifications, breast tissue age-related lobular involution and breast density
Bibliographic record
Abstract
OBJECTIVE: To demystify the potential role of vitamin D and calcium intakes in breast carcinogenesis, we explored the association between these two nutrients and three biomarkers of breast cancer risk: the presence of microcalcifications, age-related lobular involution and breast density. METHODS: A total of 82 premenopausal and 79 postmenopausal women diagnosed with breast cancer completed a food frequency questionnaire to assess their total vitamin D and calcium intakes. Presence of microcalcifications was determined by reviewing pathology reports. Age-related lobular involution was assessed in nontumoral breast tissue on hematoxylin-eosin-stained slides and percent breast density was assessed by a computer-assisted method. Multivariate generalized linear models were used to evaluate associations between quartiles of vitamin D and calcium intakes and the biomarkers of breast cancer risk. RESULTS: Increasing quartiles of vitamin D intake were inversely associated with the presence of microcalcifications (fourth quartile [Q4] prevalence ratio [PR] = 0.55; Ptrend = 0.021) and breast density (Q4-Q1 = -7.7%; Ptrend = 0.023) in postmenopausal women, and positively associated with age-related lobular involution in women with microcalcifications (Q4 PR = 1.62; Ptrend = 0.036). Increasing quartiles of calcium intake were inversely associated with microcalcifications among all (Q4 PR = 0.44), premenopausal (Q4 PR = 0.37) and postmenopausal women (Q4 PR = 0.38; Ptrend < 0.014 for all). It was also inversely associated with breast density in women without microcalcification (Q4-Q1 = -8.3%; Ptrend = 0.047), but positively associated with breast density in women with microcalcifications (Q4-Q1 = 10.0%; Ptrend = 0.032). CONCLUSIONS: Results suggest that the association between vitamin D and calcium intakes and breast cancer risk factors could be influenced by the presence of microcalcifications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".