Predictive factors of aneuploidy in infertile patients undergoing IVF: a retrospective analysis in a private IVF practice
Bibliographic record
Abstract
Abstract Background PGT-A has become an important part of IVF treatments. Despite its increased use, there are contradicting results on its role in improving reproductive outcomes of ART cycles. Given that aneuploidy is a main limiting factor for IVF success, we aimed to study the predictive factors of aneuploidy in infertile patients undergoing IVF and hence highlight the patients who would benefit the most from genetic testing. Results A retrospective analysis of 1242 blastocysts biopsied in the setting of PGT-A cycles was performed. The euploid group included 703 embryos, while the aneuploid group had 539 embryos. The factors included in the analyses were the couple’s history as well as the embryo characteristics. The primary outcome was the rate of aneuploid embryos per patient’s history as well as per embryo characteristics. The aneuploidy rate (AR) in our cohort was 43.4%. The woman’s age was found to be a significant predictor (OR 1.045, 95% CI 1.008–1.084, p = 0.016). Biopsy on day 5 as well as degree of expansion 3 was also found to affect significantly (OR 0.724, 95% CI .541–.970, p = 0.03 and OR 2.645, 95% CI 1.252–5.585, p = 0.011). Lack of consanguinity decreased the AR by an OR 0.274 with 95% CI .137–.547, p < 0.001. The number of blastocysts available, trophectoderm quality, embryo grade, gonadotropins as well as trigger used were not found to be significant predictors ( p = 0.495, 0.649, 0.264, 0.717 and 0.659 respectively). Conclusion Advanced female age, consanguinity, the day of embryo biopsy, and the degree of blastocyst expansion were all found to affect the incidence of AR. The age of the male partner, cause of infertility, and grade of embryo at biopsy were not found to correlate with aneuploidy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".