Variations in sex hormone metabolism genes, postmenopausal hormone therapy and risk of endometrial cancer
Bibliographic record
Abstract
We investigated whether variants in sex steroid hormone metabolism genes modify the effect of hormone therapy (HT) on endometrial cancer risk in postmenopausal non-Hispanic white women. A nested case-control study was conducted within the California Teachers Study (CTS). We genotyped htSNPs in six genes involved in the hormone metabolism in 286 endometrial cancer cases and 488 controls. Odds ratio (OR) and 95% confidence interval (CI) were estimated for each haplotype using unconditional logistic regression, adjusting for age. The strongest interaction was observed between duration of estrogen therapy (ET) use and haplotype 1A in CYP11A1 (p(interaction) = 0.0027; p(interaction) = 0.010 after correcting for multiple testing within each gene). The OR for endometrial cancer per copy of haplotype 1A was 2.00 (95% CI: 1.05-3.96) for long-term ET users and 0.90 (95% CI: 0.69-1.18) for never users. The most significant interaction with estrogen-progestin therapy (EPT) was found for two haplotypes on CYP19A1 and EPT use (haplotype 4A, p(interaction) = 0.024 and haplotype 3B, p(interaction) = 0.043). However, neither this interaction, nor the ET or EPT interactions for any other genes, was statistically significant after correction for multiple testing. Variations in CYP11A1 may modify the effect of ET use on risk of postmenopausal endometrial cancer; however, larger studies are needed to explore these findings further.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".