Indicators of Lifetime Estrogen Exposure: Effect on Breast Cancer Incidence and Interaction With Raloxifene Therapy in the Multiple Outcomes of Raloxifene Evaluation Study Participants
Bibliographic record
Abstract
PURPOSE: To test the hypothesis that risk factors related to lifetime estrogen exposure predict breast cancer incidence and to test if any subgroups experience enhanced benefit from raloxifene. PATIENTS AND METHODS: Postmenopausal women with osteoporosis (N = 7,705), enrolled onto the Multiple Outcomes of Raloxifene Evaluation (MORE) trial, were randomly assigned to receive placebo, raloxifene 60 mg/d, or raloxifene 120 mg/d for 4 years. Breast cancer risk was analyzed by the following baseline characteristics indicative of estrogen exposure: previous hormone replacement therapy, prevalent vertebral fractures, family history of breast cancer, estradiol level, bone mineral density (BMD), body mass index, and age at menopause. Therapy-by-subgroup interactions were assessed using a logistic regression model. RESULTS: Overall, women with the highest one-third estradiol levels (> or = 12 pmol/L) had a 2.07-fold increased invasive breast cancer risk compared with women with lower levels. Raloxifene significantly reduced breast cancer risk in both the low- and high-estrogen subgroups for all risk factors examined (P <.05 for each comparison). The women with the highest BMD and those with a family history of breast cancer experienced a significantly greater therapy benefit with raloxifene, compared with the two thirds of patients with lower BMD or those without a family history, respectively; the subgroup-by-therapy interactions were significant (P =.005 and P =.015, respectively). CONCLUSION: The MORE trial confirms that increased lifetime estrogen exposure increases breast cancer risk. Raloxifene therapy reduces breast cancer risk in postmenopausal osteoporotic women regardless of lifetime estrogen exposure, but the reduction is greater in those with higher lifetime exposure to estrogen.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".