Paternal age and adverse birth outcomes: teenager or 40+, who is at risk?
Bibliographic record
Abstract
BACKGROUND: Most previous studies on the effect of paternal age have focused on the association of advanced paternal age with congenital anomalies. The objective of this study was to determine whether paternal age is associated with the risk of adverse birth outcomes, independent of maternal confounders. METHODS: We carried out a retrospective cohort study of 2 614 966 live singletons born to married, nulliparous women aged 20-29 years between 1995 and 2000 in the USA. Multiple logistic regressions were applied to estimate the independent effect of paternal age on adverse birth outcomes. RESULTS: Compared with infants born to fathers aged 20-29 years, infants fathered by teenagers (<20 years old) had an increased risk of preterm birth [odds ratio (OR) = 1.15, 95% confidence interval (CI): 1.10, 1.20], low birth weight (OR = 1.13, 95% CI: 1.08, 1.19), small-for-gestational-age births (OR = 1.17, 95% CI: 1.13, 1.22), low Apgar score (OR = 1.13, 95% CI: 1.01, 1.27), neonatal mortality (OR = 1.22, 95% CI: 1.01, 1.49) and post-neonatal mortality (OR = 1.41, 95% CI: 1.09, 1.82). Advanced paternal age (> or =40 years) was not associated with the risk of adverse birth outcomes. CONCLUSIONS: Teenage fathers carry an increased risk of adverse birth outcomes that is independent of maternal confounders, whereas advanced paternal age is not an independent risk factor for adverse birth outcomes.
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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 | 0.003 |
| 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".