Regarding the Use of Tamoxifen Post-Oophorectomy to Prevent Hereditary Breast Cancer
Bibliographic record
Abstract
Options for the prevention of breast cancer in carriers of a BRCA1 or BRCA2 mutation include oophorectomy and tamoxifen [1]. It is possible to reduce the risk of breast cancer by 60 % by performing surgical menopause at age 40 or before [2]; however, given the projected lifetime risk of 80 % and penetrance of 30 % by age 40, this leaves a residual risk post-oophorectomy of 30%. What can be done to reduce this risk? A healthy diet should be recommended [3] but surely it is optimistic to assume that risk can be managed by diet alone. There is interest in the potential use of aromatase inhibitors in chemoprevention, but none have yet been approved in this setting, and their side effects have not yet been fully evaluated. Raloxifene and tamoxifen appear to have equivalent effects in chemoprevention in women at
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".