Maternal age at first birth and uterine cancer risk: A comprehensive analysis using NHANES data (2003–2018)
Bibliographic record
Abstract
• The study analyzed data from 7095 females using NHANES (2003–2018). • Adjusted odds ratios suggested reduced but non-significant odds of uterine cancer for later first births. • Results were similar with multiple imputation to address confounder missingness. • A sensitivity analysis with post-menopausal females also showed no association. Several reproductive factors, including parity and age at menarche, have been identified as risk factors for uterine cancers. However, the association between maternal age at first birth and uterine cancer remains conflicting. This cross-sectional study included females aged 20 years and older with at least one live birth across eight National Health and Nutrition Examination Survey (NHANES) cycles (2003–2018). We used design-adjusted logistic regression, with multiple imputation for missing data, to explore the association of age at first birth and uterine cancer. As a sensitivity analysis, the sample was restricted to post-menopausal females; logistic regression analyses were repeated. Among 7095 participants, 104 had uterine cancer. The adjusted odds ratio (aOR) for uterine cancer for participants with a first live birth at ≥25 years was 0.66 (95 % confidence interval (CI): 0.33–1.35) compared to those with a first birth at <20 years. For participants with a first birth between 20–24 years, the aOR was 0.93 (95 % CI: 0.51–1.69). Multiple imputation and sensitivity analyses yielded similar non-significant results. Our findings suggest no statistically significant association between maternal age at first live birth and uterine cancer, aligning with existing literature. Further research is needed to explore other reproductive factors and their role in uterine cancer risk.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".