Hip bone density predicts breast cancer risk independently of Gail score
Bibliographic record
Abstract
BACKGROUND: The Gail model has been commonly used to estimate a woman's risk of breast cancer within a certain time period. High bone mineral density (BMD) is also a significant risk factor for breast cancer, but it appears to play no role in the Gail model. The objective of the current study was to investigate whether hip BMD predicts postmenopausal breast cancer risk independently of the Gail score. METHODS: In this prospective study, 9941 postmenopausal women who had a baseline hip BMD and Gail score from the Women's Health Initiative were included in the analysis. Their average age was 63.0 +/- 7.4 years at baseline. RESULTS: After an average of 8.43 years of follow-up, 327 incident breast cancer cases were reported and adjudicated. In a multivariate Cox proportional hazards model, the hazards ratios (95% confidence interval [95% CI]) for incident breast cancer were 1.35 (95% CI, 1.05-1.73) for high Gail score (>or=1.67%) and 1.25 (95% CI, 1.11-1.40) for each unit of increase in the total hip BMD T-score. Restricting the analysis to women with both BMD and a Gail score above the median, a sharp increase in incident breast cancer for women with the highest BMD and Gail scores was found (P < .05). CONCLUSIONS: The contribution of BMD to the prediction of incident postmenopausal breast cancer across the entire population was found to be independent of the Gail score. However, among women with both high BMD and a high Gail score, there appears to be an interaction between these 2 factors. These findings suggest that BMD and Gail score may be used together to better quantify the risk of breast cancer.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".