Paternal age predicts live birth in women above 40 years of age undergoing in-vitro fertilization (IVF)
Bibliographic record
Abstract
Purpose: To determine which factors predict pregnancy outcome in women aged 40 years and above who underwent in-vitro fertilization. Method: We conducted a retrospective case-control study of 631 women aged 40–46 years, who underwent a total of 904 IVF cycles with autologous gametes. We used stepwise logistic regression analysis to develop predictors of pregnancy, clinical pregnancy and live birth outcomes. Data are presented as mean ± SD, percentage and confidence intervals. Results: Predictors of live birth included maternal (95% CI: 0.36–0.78) and paternal (95% CI: 0.62–0.94) age, the number of follicels > 14 mm (95% CI: 1.2–3.2), the number of oocytes collected (95% CI: 1.3–2.9) the number of metaphase II oocytes (95% CI: 1.3–2.4) and the number of cleavage stage embryos (95% CI: 1.8–2.6). The predictors of pregnancy and clinical pregnancy were similar but did not include male age (P > 0.05). To further determine the role of male age in live birth a control group of women younger than 40 years was collected. Male age was not a significant predictor of live birth among younger women (P = 0.42). Conclusions: Female age and better ovarian stimulation were confirmed as predictors of outcomes in older women doing IVF. However, male age was also noted to be a significant individual predictor of live birth in women over 40 years of age, but not in younger women doing IVF.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".