Fertility Preservation with Immature and in Vitro Matured Oocytes
Bibliographic record
Abstract
The development of an effective oocyte-freezing program will have a major impact on clinical practice in reproductive medicine and will serve as a powerful tool to preserve fertility for teenage girls and young women without male partners or for those individuals who are affected by malignancies. It will also be beneficial to infertile couples who have moral or religious objections about embryo cryopreservation. In addition, a successful oocyte cryopreservation program will eliminate the need for donor and recipient menstrual cycle synchronization and will enable the establishment of oocyte banks, which would facilitate the logistics of coordinating egg donors with recipients. Recent advances in vitrification technology have markedly improved the oocyte survival rate after thawing, and the pregnancy rate is comparable with that achieved with fresh oocytes. However, most studies were performed using in vivo matured oocytes for vitrification. The objective of this article was to review whether immature and in vitro matured human oocytes can be vitrified successfully. The results indicated that although healthy live births can be achieved from the combination of in vitro maturation (IVM) oocytes and vitrification, vitrification of in vitro matured oocytes is less effective than vitrification of in vivo matured oocytes. The results suggest that oocytes should be vitrified at the mature metaphase II stage following IVM rather than at the immature germinal vesicle (GV) stage because the potential of oocyte maturation is reduced by the vitrification of immature oocytes at the GV stage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".