The perinatal outcomes of frozen-thawed blastocyst transfer are better than fresh blastocyst transfer: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Transfer of cryopreserved-warmed blastocysts is common in the practice of in vitro fertilization. The purpose of the study is to examine the available evidence and determine whether cryopreservation of blastocysts and subsequent frozen blastocyst transfer (BT) result in better outcomes than fresh BT. MATERIAL AND METHODS: Related studies comparing outcomes of in vitro fertilization (IVF) cycles between fresh and frozen BTs were retrieved from Medline, Cochrane Central Register of Clinical Trials, EMBASE, DARE, and CINAHL through March 2020. The outcomes of interest were preterm birth, extremely preterm birth, small for gestational age, large for gestational age, low birth weight, extremely low birth weight, caesarean section, perinatal mortality and preeclampsia. The analysis was performed using Rev Man 5.1 software. Risk ratios (RRs) and risk differences were calculated with 95% confidence intervals, to evaluate the results of each outcome. The quality of the referenced studies was assessed using the Newcastle-Ottawa scale (NOS) checklists. RESULTS: Nine studies with 42,342 women incorporated in this meta-analysis. The incidence of preterm birth (RR = 0.89, 95% CI: 0.82, 0.97) and small for gestational age (RR = 0.55, 95% CI 0.41-0.74) was low in frozen BT group. There was no significant difference in the risk of low birth weight (RR = 0.89, 95% CI: 0.67, 1.19) and perinatal mortality (RR = 1.47, 95% CI: 0.85, 2.55) between frozen-thawed and fresh BT. Singleton pregnancy after frozen BT was associated with higher large for gestational age (RR = 1.47, 95% CI: 1.37, 1.57), caesarean section rates (RR = 1.24, 95% CI: 1.13, 1.36) and preeclampsia compared with fresh BT (RR = 1.85, 95% CI: 1.22, 2.82). CONCLUSIONS: The frozen BT results in better perinatal outcomes compared with that of fresh BT. Furthermore, comprehensive randomized clinical trials comparing freeze-all with fresh BT cycles are needed to draw sound conclusions.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".