Lack of evidence that progesterone in ovulatory cycles causes breast cancer
Bibliographic record
Abstract
A recent Perspective article asserted that progesterone secretion during ovulatory cycles is the cause of breast cancer. However, we challenge most of the evidence developed in this publication. First, there is a lack of evidence that progesterone is mutagenic for breast cells. Cause of a cancer should mean initiation by mutation, as opposed to promotion. Second, subclinical ovulatory disturbances occur rather frequently in normal-length menstrual cycles. Third, the authors attribute a potential carcinogenic effect to progesterone secreted during menstrual cycles but not to progesterone during pregnancy. They did not discuss breast cancer evidence from progesterone/progestin therapeutics. They argue that in genetic primary amenorrhea, a hypothetic lower risk of breast cancer could be due to the lack of progesterone, despite the progesterone/progestin in hormone replacements these women receive. Fourth, they advocate a regulatory effect of progesterone on several genes potentially involved in cancer genesis. In particular, they attribute a lower risk of breast cancer in women with Mayer-Rokitansky-Küster-Hauser syndrome to a defect in the progesterone-stimulated Wnt4 gene. However, this defect is only present in a small subset. Thus, the postulated progesterone breast cancer risk is unconvincing, which we discuss point by point in this commentary.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".