Freezing eggs to get ahead: a look at oocyte cryopreservation for non-medical reasons
Bibliographic record
Abstract
The age of first pregnancies for women has been on the rise, partly due to prioritization of career development. Maternal aging is a significant factor affecting fertility, and is correlated with infertility and several adverse pregnancy outcomes. Oocyte cryopreservation (OC), currently recommended to cancer patients pending treatments affecting fertility, is now being explored as an option for extending female fertility due to its efficacy in in vitro fertilization (IVF). However, there is a paucity of data confirming the superiority of the procedure over natural pregnancy in healthy women, given the potential complications. Caution in recommending the procedure should therefore be taken. RésuméL’âge des premières grossesses chez les femmes a augmenté, en partie à cause de la hiérarchisation du développement de carrière. Le vieillissement maternel est un facteur important affectant la fertilité, et est corrélé avec l’infertilité et plusieurs résultats défavorables de la grossesse. La cryoconservation des ovocytes (CO), recommandée aux patients cancéreux en attendant les traitements affectant la fertilité, est actuellement explorée comme une option pour augmenter la fertilité féminine en raison de son efficacité dans la fécondation in vitro (FIV). Cependant, il y a un manque de données confirmant la supériorité de la procédure sur la grossesse naturelle chez les femmes en bonne santé, étant donné les complications potentielles. La prudence dans la recommandation de la procédure devrait donc être prise.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".