[Breast density: a biomarker to better understand and prevent breast cancer].
Bibliographic record
Abstract
In Quebec, cancer is the principal cause of mortality. This epidemiologic research program includes two components. The first component takes place at the "Institut national de santé publique du Québec" and involves surveillance and evaluation of practices in oncology with the aim of providing the Quebec Ministry of Health with some of the evidence needed to determine its policies in cancer control. The second component takes place at the "Unité de recherche en santé des populations (URESP)" of Laval University and is devoted to studying the etiology and prevention of breast cancer. This paper focuses on this second research component which uses mammographic breast density as an intermediate biomarker to study the causes of breast cancer and strategies to prevent it. Breast cancer risk is much higher among women with very dense breasts than among those with little or no breast density. Recently, we were among the first to show that women with high vitamin D or calcium intakes have less breast density than those with low intakes, especially among premenopausal women. Furthermore, we have confirmed that breast density was increased among premenopausal women with high levels of IGF-I and low levels of IGFBP3 which is consistent with the observed effect of these molecules on breast cancer risk. Studies are now being conducted to assess whether breast density varies according to blood levels of vitamin D and of additional growth factors, as well as to genetic polymorphisms involved in the pathways of vitamin D, calcium and growth factors. The increase in vitamin D and calcium intakes may prove to be a safe and inexpensive approach to breast cancer prevention; this possibility should be carefully examined as quickly as possible.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".