Hormonal and Reproductive Factors and Pancreatic Cancer Risk
Bibliographic record
Abstract
OBJECTIVES: We examined pancreatic cancer risk in association with hormonal and reproductive factors in a prospective cohort study of 89,835 Canadian women, aged 40-59 at recruitment, who were enrolled in the National Breast Screening Study (NBSS). METHODS: Linkages to national cancer and mortality databases yielded data on cancer incidence and deaths of all causes, respectively, with follow-up ending between 1998 and 2000. Cox proportional hazards models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for the association between hormonal and reproductive factors and risk of pancreatic cancer. RESULTS: During a mean of 16.4 years of follow-up, we observed 187 incident pancreatic cancer cases. Compared with women who were premenopausal at baseline, postmenopausal women were at significantly increased risk of pancreatic cancer (odds ratio = 2.44, 95% confidence interval [CI] = 1.45-4.09). Age at first livebirth, parity, age at menarche, use of oral contraceptive, and use of hormone replacement therapy (HRT) were not associated with altered pancreatic cancer risk in our study population. However, among parous women, risk increased with increasing parity. CONCLUSION: Other than the increased risk among postmenopausal women, the present study provides little support for associations with hormonal factors. Additional prospective data are 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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".