Expansion and herniation: evaluation of the best pregnancy rate predictor after quarter laser assisted hatching in frozen blastocyst transfers
Bibliographic record
Abstract
OBJECTIVE: To assess the recovery of thawed blastocysts submitted to quarter laser assisted hatching and examine potential correlations between the procedure and pregnancy rates. METHODS: This cross-sectional study included only single-blastocyst transfers performed from July 2017 to December 2018. A total of 765 blastocysts were thawed and immediately submitted to quarter laser assisted hatching in the zona pellucida; they were subsequently incubated for three hours until transfer time, at which time they were examined for collapse or expansion; expanded blastocysts were further evaluated for herniation. The Chi-square test was used in statistical analysis. RESULTS: 627 blastocysts expanded (81.9%) and yielded a pregnancy rate of 40% (251/627). 138 blastocysts collapsed after thawing (18.0%) and yielded a pregnancy rate of 25.4% (35/138) (p=0.001). Additional analysis of the subgroup of expanded blastocysts revealed that the 385 herniated blastocysts (61.4%) yielded a pregnancy rate of 43.9% (169/385). The remaining 242 non-herniated blastocysts (38.6%) yielded a pregnancy rate of 33.9% (82/242) (p=0.013). Statistical significance was attributed to events with a p<0.05. CONCLUSION: Quarter laser assisted hatching is a safe, valid, and relatively easy-to-use procedure for thawed blastocysts. Blastocysts that expanded and herniated after quarter laser assisted hatching presented statistically superior results.
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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 | 0.005 |
| 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".