Bioidentical, Synthetic, and Animal Based Hormone Replacement Therapies and Risk of Breast Cancer [1M]
Bibliographic record
Abstract
INTRODUCTION: Hormone Replacement Therapy (HRT) has been associated with an increased risk of breast cancer. Our study objective was to evaluate whether the increased risk is dependent on the formulation used. METHODS: We carried out a population-based case-control study using data from the United Kingdom Clinical Practice Research Datalink on women aged >50. Newly diagnosed breast cancer cases were age-matched with women of comparable follow-up time with no history of breast cancer at a 1:10 ratio. Exposures were classified as ever/never for the HRT formulations: Bioidentical Estrogens (BE), Conjugated Equine Estrogens (CEE), Micronized Progesterone (MP), and Synthetic Progestogens (SP). Logistic regression estimated adjusted effects of HRT formulation on breast cancer. RESULTS: Between 1995-2014, 43,183 cases of breast cancer were identified and matched to 431,830 controls. Compared with women who had never used HRT, HRT use was associated with an overall increased risk of breast cancer OR 1.12 (1.09-1.15), p < 0.0001. Compared to never users, estrogens were not associated with breast cancer: BE (OR 1.04 (1.00-1.09), p=0.07), CEE (OR 1.01 (0.96-1.05), p=0.78), both (OR 1.32 (0.30-5.77), p=0.25). As compared to never users, progestogens appeared differentially associated with breast cancer: MP (OR 0.99 (0.55-1.79), p=0.98), SP (OR 1.28 (1.22-1.35), p < 0.0001), both (OR 1.32 (0.30-5.77), p=0.72). CONCLUSION: While HRT use is associated with an overall increased risk of breast cancer, this association appears to be uniquely in women having been given synthetic progestogens. Synthetic progestogens should not be given as part of HRT and counseling regarding the risk of breast cancer should consider the effect of formulation used.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".