25-Hydroxy vitamin-D, obesity, and associated variables as predictors of breast cancer risk and tamoxifen benefit in NSABP-P1
Bibliographic record
Abstract
Observational studies suggest that host factors are associated with breast cancer risk. The influence of obesity, vitamin-D status, insulin resistance, inflammation, and elevated adipocytokines in women at high risk of breast cancer is unknown. The NSABP-P1 trial population was used for a nested case-control study. Cases were drawn from those who developed invasive breast cancer and controls selected from unaffected participants (≤4 per case) matched for age, race, 5 year Gail score, and geographic location of clinical center as a surrogate for latitude. Fasting serum banked at trial enrolment was assayed for 25-hydroxy vitamin-D (25OHD), insulin, leptin (adipocytokine), and C-reactive protein (CRP, marker of inflammation). Logistic regression was used to test for associations between study variables and the risk of invasive breast cancer. Two hundred and thirty-one cases were matched with 856 controls. Mean age was 54, and 49% were premenopausal. There were negative correlations for 25OHD with body mass index (BMI), insulin, CRP, and leptin. BMI ≥ 25 kg/m(2) was associated with higher breast cancer risk (odds ratio [OR] 1.45, p = 0.02) and tamoxifen treatment was associated with lower risk (OR = 0.44, p < 0.001). Suboptimal 25OHD (<72 nmol/l) did not influence breast cancer risk (OR = 1.06, p = 0.76). When evaluated as continuous variables, 25OHD, insulin, CRP, and leptin levels were not associated with breast cancer risk (all p > 0.34). In this high risk population, higher BMI was associated with a greater breast cancer risk. Serum levels of 25OHD, insulin, CRP, and leptin were not independent predictors of either breast cancer risk or tamoxifen benefit.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".