Ovulation Induction for Infertility the Risk of Breast Cancer: A Population-Based Case-Control Study [11B]
Bibliographic record
Abstract
INTRODUCTION: There is a well-established interplay between reproductive hormone levels and breast cancer. Questions remain in regards to the potential ovulation-inducing fertility treatments, which elevate serum estrogen and progesterone to supraphysiologic levels the treatment period, to affect breast tumorigenesis. METHODS: A population-based case-control study using the Clinical Practice Research Datalink (CPRD) was performed. All cases of breast cancer occurring between 1995-2013 were matched with 10 age-matched controls. Cases and controls were assessed for exposure to clomiphene or in-vitro fertilization (IVF) via their clinical records. Odds ratios and 95% confidence intervals for each exposure were computed using multivariate logistic regression, adjusted for smoking, alcohol use, BMI, oral contraceptive use, hormone replacement therapy and oophorectomy. RESULTS: The study population consisted of 27,026 cases of breast cancer and their matched controls, with 1,717 subjects having a history of clomiphene exposure and 1,137 subjects having a history of IVF. Ever-exposure to clomiphene was significantly associated with breast cancer, OR 1.34 95% CI [1.26-1.44]. IVF-exposure was similarly associated with breast cancer, OR 1.52 95% CI [1.40-1.65]. The stratified analysis indicated clomiphene was associated with pre-menopausal cancers (OR 1.59 95% CI [1.47-1.73]) but not post-menopausal malignancies (OR 0.94 95% CI [0.83-1.06]). IVF was associated with both pre- and post-menopausal cancers. CONCLUSION: Ovulation induction with clomiphene citrate and gonadotropins increases the risk of breast cancer. Women should be advised of this elevated risk understanding the associated implications.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".