Neonatal outcomes in singleton pregnancies conceived by fresh or frozen embryo transfer compared to spontaneous conceptions: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Purpose The use of assisted reproductive technology (ART) has increased in the last 2 decades and continuous surveillance is needed. This systematic review aims to assess the risk of adverse neonatal outcomes (preterm birth [PTB], low birth weight [LBW], small-for-gestationalage [SGA] and large for gestational-age [LGA]), in singleton pregnancies conceived by fresh or frozen embryo transfer (FET) compared to spontaneous conceptions. Methods Cohort studies were identified from MEDLINE, Embase, Cochrane Library (January 2019), and manual search. Meta-analyses were performed to estimate odds ratios (OR) using random effects models in RevMan 5.3 and I -squared ( I 2 ) test > 50% was considered as high heterogeneity. Results After 3142 titles and abstracts were screened, 1180 full-text articles were assessed, and 14 were eligible. For fresh embryo transfer, the pooled ORs were PTB 1.64 (95% CI 1.46, 1.84); I 2 = 97%; LBW 1.67 (95% CI 1.52, 1.85); I 2 = 94%; SGA 1.46 [95% CI 1.11, 1.92]; I 2 = 99%, LGA 0.88 (95% CI 0.80, 0.87); I 2 = 80%). For frozen, the pooled ORs were PTB 1.39 (95% CI 1.34, 1.44); I 2 = 0%; LBW 1.38 (95% CI 0.91, 2.09); I 2 = 98%; SGA 0.83 (95% CI 0.57, 1.19); I 2 = 0%, LGA 1.57 (95% CI 1.48, 1.68); I 2 = 22%). Conclusions When compared with spontaneous pregnancies, fresh, but not frozen was associated with LBW and SGA. Both fresh and frozen were associated with PTB. Frozen was uniquely associated with LGA. Despite improvements in ART protocols in relation to pregnancy rates, attention is needed towards monitoring adverse neonatal outcomes in these pregnancies.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.035 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".