Lower testosterone as a cause of endometriosis
Bibliographic record
Abstract
Two recent genomic studies have demonstrated that lower genetically-predicted testosterone is associated with higher risk of endometriosis. These results are concordant with a suite of additional findings, including: (1) links of endometriosis and its correlates with shorter anogenital distance (an indicator of lower prenatal testosterone), (2) higher pain sensitivity under lower testosterone in humans and animal models, (3) associations of lower testosterone levels with key endocrine correlates of endometriosis, including lower antimullerian hormone, luteinizing hormone, and β-endorphin, and (4) data from animal models showing that development under lower prenatal testosterone results in shorter anogenital distances, earlier onset of estrus, and more-regular ovulatory cycles. Taken together, this evidence supports a 'two-hit' model of endometriosis, whereby low prenatal testosterone differentially programs the hypothalamic-pituitary-ovarian axis in such a way as to increase vulnerability, and low adult testosterone, combined with early menarche, high pain sensitivity, and other genetic and environmental factors, leads to increased rates of endometriosis in high-risk women. Most importantly, these genetic and phenotypic findings suggest that endometriosis represents a disorder of low testosterone as well as high estrogen, and indicate that additional studies of testosterone levels and effects in women with endometriosis are urgently needed.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".