The impact of lateral placenta on preeclampsia and small for gestational age neonates: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Objectives We conducted a systematic review and meta-analysis to quantitatively summarize the present data on the association of prenatally identified lateral placenta in singleton pregnancies with small for gestational age (SGA) neonates, preeclampsia and other perinatal outcomes. Methods From inception to November 2021, we searched PubMed/Medline, Scopus and The Cochrane Library for papers comparing the risk of SGA and preeclampsia, as well as other perinatal outcomes in singleton pregnancies with a prenatally identified lateral placenta to those with non-lateral placentas. The revised Newcastle-Ottawa Scale was used to evaluate the quality of eligible papers. The I2 test was employed to evaluate the heterogeneity of outcomes among the studies. To investigate the possibility of publication bias, funnel plots were constructed. Prospero RN: CRD42021251590. Results The search yielded 5,420 articles, of which 16 were chosen, comprising of 15 cohort studies and one case control study with a total of 4,947 cases of lateral and 96,035 of non-lateral placenta (controls) reported. SGA neonates were more likely to be delivered in cases with a lateral placenta (OR: 1.74; 95% CI: 1.54–1.96; p<0.00001; I2=47%). Likewise, placental laterality was linked to a higher risk of fetal growth restriction (OR: 2.18; 95% CI: 1.54–3.06; p<0.00001; I2=0%), hypertensive disorders of pregnancy (OR: 2.39; 95% CI: 1.65–3.51; p=0.0001; I2=80%), preeclampsia (OR: 2.92; 95% CI: 1.92–4.44; p<0.0001; I2=82%) and preterm delivery (OR: 1.65; 95% CI: 1.46–1.87; p<0.00001; I2=0%). Conclusions The prenatal diagnosis of a lateral placenta appears to be associated with a higher incidence of preeclampsia, fetal growth restriction, preterm delivery and SGA. This may prove useful in screening for these conditions at the second trimester anomaly scan.
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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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.032 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".