Vitamin D and Reduced Risk of Breast Cancer: A Population-Based Case-Control Study
Bibliographic record
Abstract
BACKGROUND: Vitamin D, antiproliferative and proapoptotic in breast cancer cell lines, can reduce the development of mammary tumors in carcinogen-exposed rats. Current evidence in humans is limited with some suggestion that vitamin D-related factors may reduce the risk of breast cancer. We conducted a population-based case-control study to assess the evidence for a relationship between sources of vitamin D and breast cancer risk. METHODS: Women with newly diagnosed invasive breast cancer were identified from the Ontario Cancer Registry. Women without breast cancer were identified through randomly selected residential telephone numbers. Telephone interviews were completed for 972 cases and 1,135 controls. Odds ratios (OR) and 95% confidence intervals (CI) for vitamin D-related variables were estimated using unconditional logistic regression with adjustment for potential confounders. RESULTS: Reduced breast cancer risks were associated with increasing sun exposure from ages 10 to 19 (e.g., OR, 0.65; 95% CI, 0.50-0.85 for the highest quartile of outdoor activities versus the lowest; P for trend = 0.0006). Reduced risk was also associated with cod liver oil use (OR, 0.76; 95% CI, 0.62-0.92) and increasing milk consumption (OR, 0.62 95% CI 0.45-0.86 for >or=10 glasses per week versus none; P for trend = 0.0004). There was weaker evidence for associations from ages 20 to 29 and no evidence for ages 45 to 54. CONCLUSION: We found strong evidence to support the hypothesis that vitamin D could help prevent breast cancer. However, our results suggest that exposure earlier in life, particularly during breast development, maybe most relevant. These results should be confirmed.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".