Placenta previa and the risk of intrauterine growth restriction (IUGR): a systematic review and meta-analysis
Bibliographic record
Abstract
Background Whether placental location confers specific neonatal risks is controversial. In particular, whether placenta previa is associated with intra-uterine growth restriction (IUGR)/small for gestational age (SGA) remains a matter of debate. Methods We searched Medline, EMBASE, Google Scholar, Scopus, ISI Web of Science and Cochrane database search, as well as PubMed (www.pubmed.gov) until the end of December 2018 to conduct a systematic review and meta-analysis to determine the risk of IUGR/SGA in cases of placenta previa. We defined IUGR/SGA as birth weight below the 10th percentile, regardless of the terminology used in individual studies. Risk of bias was assessed using the Cochrane Handbook for Systematic Reviews of Interventions. We used odds ratios (OR) and a fixed effects (FE) model to calculate weighted estimates in a forest plot. Statistical homogeneity was checked with the I2 statistic using Review Manager 5.3.5 (The Cochrane Collaboration, 2014). Results We obtained 357 records, of which 13 met the inclusion criteria. All study designs were retrospective in nature, and included 11 cohort and two case-control studies. A total of 1,593,226 singleton pregnancies were included, of which 10,575 had a placenta previa. The incidence of growth abnormalities was 8.7/100 births in cases of placenta previa vs. 5.8/100 births among controls. Relative to cases with alternative placental location, pregnancies with placenta previa were associated with a mild increase in the risk of IUGR/SGA, with a pooled OR [95% confidence interval (CI)] of 1.19 (1.10-1.27). Statistical heterogeneity was high with an I2 = 94%. Conclusion Neonates from pregnancies with placenta previa have a mild increase in the risk of IUGR/SGA.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".