Hormonal and reproductive factors and risk of glioma: A prospective cohort study
Bibliographic record
Abstract
The etiology of glioma, the most commonly diagnosed malignant brain tumor among adults in the United States, is poorly understood. Given the lower incidence rate of glioma in women than in men, it has been hypothesized that reproductive and hormonal factors may be involved in the etiology of glioma. We conducted a secondary analysis of data from the National Breast Screening Study, which included 89,835 Canadian women, aged 40-59 years at recruitment between 1980 and 1985. Linkages to national cancer and mortality databases yielded data on cancer incidence and deaths from all causes, respectively, with follow-up ending between 1998 and 2000. Cox proportional hazards models were used to estimate hazard ratios and 95% confidence intervals (CI) for the association between hormonal and reproductive factors and risk of glioma. During a mean of 16.4 years of follow-up, we observed 120 incident glioma cases. Compared with women with a relatively early age at menarche (< or =12 years), women who were 13-14 years of age at menarche had a 64% increased risk of glioma (95% CI = 1.01-2.65), and women who were older than 14 years of age at menarche had a 66% increased risk of glioma (95% CI = 0.86-3.20, p(trend) = 0.06). Age at first live birth, parity, menopausal status, use of oral contraceptive and use of hormone replacement therapy were not associated with altered glioma risk in our study population. Additional prospective studies are needed to confirm our findings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".