Placenta morphology and biomarkers in pregnancies with congenital heart disease – A systematic review
Bibliographic record
Abstract
Impaired placentation is an important contributing factor to intra-uterine growth restriction and pre-eclampsia in fetuses with congenital heart defects (CHD). These pregnancy complications occur more frequently in pregnancies with fetal CHD. One of the most important factors influencing the life of children with CHD is neurodevelopmental delay, which seems to start already in utero. Delayed neurodevelopment in utero may be correlated or even (partly) explained by impaired placentation in CHD cases. This systematic review provides an overview of published literature on placental development in pregnancies with fetal CHD. A systematic search was performed and the Newcastle-Ottawa scale was used to access data quality. Primary outcomes were placenta size and weight, vascular and villous architecture, immunohistochemistry, angiogenic biomarkers and/or placental gene expression. A total of 1161 articles were reviewed and 21 studies were included. Studies including CHD with a genetic disorder or syndrome and/or multiple pregnancies were excluded. Lower placental weight and elevated rates of abnormal umbilical cord insertions were found in CHD. Cases with CHD more frequently showed microscopic placental abnormalities (i.e. abnormal villous maturation and increased maternal vascular malperfusion lesions), reduced levels of angiogenic biomarkers and increased levels of anti-angiogenic biomarkers in maternal serum and umbilical cord blood. Altered gene expression involved in placental development and fetal growth were found in maternal serum and CHD placentas. In conclusion, abnormal placentation is found in CHD. More extensive studies are needed to elucidate the contribution of impaired placentation to delayed neurodevelopment in CHD cases.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".