P4-10-02: A Meta-Analysis of the Association of Blood Levels of Vitamin-D and the Risk of Breast Cancer.
Bibliographic record
Abstract
Abstract Background: A considerable body of literature has examined the association of vitamin-D with breast cancer risk and the potential role in its prevention. Geographic studies show higher incidence of breast cancer in patients residing at high latitudes. Other data linking vitamin-D deficiency to breast cancer risk are inconsistent. Materials and Methods: A literature based meta-analysis was conducted. Odds ratios (OR) for breast cancer based on blood levels of 25-hydroxy or 1,25-hydroxy vitamin-D were computed and pooled. Analysis was conducted separately for studies where blood levels were taken before (group A) or after (group B) breast cancer diagnosis. Results: Thirteen studies were identified. Nine studies were included in group A and 4 studies included in group B. For group A, there was no significant association between lower vitamin-D levels and breast cancer risk (pooled OR = 1.09, 95% confidence intervals 0.99−1.20, p=0.08). For group B, there was a highly significant association between lower vitamin-D levels and breast cancer (pooled OR = 2.81, 95% confidence intervals 1.70−4.65, p<0.001). The test for interaction between groups was highly significant (p<0.001). When all studies were pooled, the OR was 1.38 (95% confidence intervals 1.13−1.70, p=0.002). Conclusion: When measured before breast cancer diagnosis, blood levels of vitamin-D are not associated with breast cancer risk. Breast tumors have been shown to differentially express vitamin-D hydroxylase. Therefore, any association of vitamin-D and breast cancer in studies measuring blood levels after breast cancer diagnosis may be confounded by reverse causation bias. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P4-10-02.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.058 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".