Assisted hatching of vitrified-warmed blastocysts prior to embryo transfer does not improve pregnancy outcomes
Bibliographic record
Abstract
OBJECTIVE: This study aims to determine the impact of assisted hatching (AH) on pregnancy outcomes in vitrified-warmed blastocyst transfers, and evaluate if embryo expansion or morphology influences outcomes. METHODS: A retrospective cohort study was performed including vitrified-warmed blastocyst transfers at our clinic between 2013 and 2017. Of the 2165 embryo transfers, 1986 underwent laser AH and 179 were non-assisted hatched (NAH). The primary outcome was live birth. Secondary outcomes included conception, implantation, clinical pregnancy, clinical pregnancy loss, and monozygotic twinning (MZT). RESULTS: AH and NAH groups had similar rates of conception (38.7% vs 42.1%), implantation (26.2% vs 27.3%), clinical pregnancy (29.1% vs 30.3%), clinical pregnancy loss (24.0% vs 17.8%), live birth (19.9% vs 20.5%), and MZT (2.08% vs 2.86%). Five pairs of dichorionic/diamniotic twins resulted from single embryo transfers. AH of embryos with expansion grades ≤3 was associated with lower rates of conception (32.5% vs 44.3%%, p < 0.05) and clinical pregnancy (24.0% vs 32.8%, p < 0.05). CONCLUSION: AH prior to transfer of vitrified-warmed blastocysts was not associated with improved pregnancy outcomes. The identification of dichorionic/diamniotic twins from single blastocyst transfers challenges the previously held notion that dichorionic/diamniotic MZTs can only occur from division prior to the blastocyst stage. Prospective studies are needed to validate the novel finding of lower rates of conception and clinical pregnancy after AH in embryos with lower expansion grade.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".