In Vitro Fertilization and Placenta Accreta (spectrum): A Systematic Review
Bibliographic record
Abstract
In vitro fertilization (IVF) has transformed infertility treatment, providing options for individuals struggling to conceive naturally, with significant success rates, particularly in women under 35. However, IVF pregnancies are associated with potential obstetric complications, notably placenta accreta spectrum (PAS), which necessitates early diagnosis and a multidisciplinary approach to safeguard maternal-fetal health. This systematic review involved a comprehensive search of databases such as PubMed, Cochrane Library, BVS, Embase, Web of Science, and Scopus using specific terms related to IVF and PAS. Original studies comparing spontaneous pregnancies to IVF pregnancies were included, while case reports and non-peer-reviewed articles were excluded. Data extraction was standardized, and study quality was assessed using the Newcastle-Ottawa tool. The analysis covered thirteen articles involving 252,295 assisted reproductive technology pregnancies and over 16 million spontaneous pregnancies. A key finding is the increased risk of PAS in IVF pregnancies, with some studies indicating that frozen embryo transfer (FET) poses a higher risk than fresh embryo transfer. Additional complications include hypertension, preeclampsia, and intrauterine growth restriction (IUGR). Interestingly, some research suggests a lower Intensive Care Unit admission rate for women with placenta accreta after IVF compared to those with spontaneous conception. This indicates that while assisted reproductive technology is an independent risk factor for PAS, it presents a unique risk profile. The review emphasizes the need for further research into maternal outcomes related to PAS after assisted reproductive techniques (ART) and highlights the importance of specialized follow-up and rigorous prenatal diagnosis to minimize complications 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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".