Having a baby in your 40s with assisted reproductive technology: The reproductive dilemma of autologous versus donor oocytes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it