Why we should not select the faster embryo: lessons from mice and cattle
Bibliographic record
Abstract
Many studies have shown that in vitro culture can negatively impact preimplantation development. This necessitates some selection criteria for identifying the best-suited embryos for transfer. That said, embryo selection after in vitro culture remains a subjective process in most mammalian species, including cows, mice and humans. General consensus in the field is that embryos that develop in a timely manner have the highest developmental competence and viability after transfer. Herein lies the key question: what is a timely manner? With emerging data in bovine and mouse supporting increased developmental competency in embryos with moderate rates of development, it is time to question whether the fastest developing embryos are the best embryos for transfer in the human clinic. This is especially relevant to epigenetic gene regulation, including genomic imprinting, where faster developing embryos exhibit loss of imprinted methylation, as well as to sex selection bias, where faster developmental rates of male embryos may lead to biased embryo transfer and, in turn, biased sex ratios. In this review, we explore evidence surrounding the question of developmental timing as it relates to bovine embryo quality, mouse embryo quality and genomic imprint maintenance, and embryo sex.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".