Having a baby in your 40s with assisted reproductive technology: The reproductive dilemma of autologous versus donor oocytes
Bibliographic record
Abstract
BACKGROUND: Increasing numbers of women ≥40 years old are accessing assisted reproductive technology (ART) due to age-related infertility. There is limited population-based evidence about the impact on the cumulative live birth rate (CLBR) of women aged ≥40 years using their own oocytes, compared to women of a similar age, using donor oocytes. AIMS: To compare the CLBR for women ≥40 years undergoing ART using autologous oocytes and women of similar age using donor oocytes. MATERIALS AND METHODS: This population-based retrospective cohort study used data from all women aged ≥40 years undergoing ART with donated (n = 987) or autologous oocytes (n = 19 170) in Victoria, Australia between 2009 and 2016. A discrete-time survival model was used to evaluate the CLBR following ART with donor or autologous oocytes. The odds ratio, adjusted for woman's age; male age; parity; cause of infertility; and the associated 95% confidence intervals (CI), were calculated. The numbers needed to be exposed (NNEs) were calculated from the adjusted odds ratio (aOR) and the CLBR in the autologous group. RESULTS: The CLBR ranged from 28.6 to 42.5% in the donor group and from 12.5% to 1.4% in the autologous group. The discrete-time survival analysis with 95% CI demonstrated significant aOR on CLBR across all ages (range aOR: 2.56, 95% CI: 1.62-4.01 to aOR: 15.40, 95% CI: 9.10-26.04). CONCLUSIONS: Women aged ≥40 years, using donor oocytes had a significantly higher CLBR than women using autologous oocytes. The findings can be used when counselling women ≥40 years about their ART treatment options and to inform public policy.
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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 | 0.018 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".