Effects of external cephalic version for breech presentation at or near term in high-resource settings: A systematic review of randomized and non-randomized studies
Bibliographic record
Abstract
INTRODUCTION: External cephalic version (ECV) for breech presentation involves manual manipulation of the fetus from breech to cephalic presentation at or near term, in an attempt to avoid breech birth. This systematic review summarizes the literature on the effects of ECV at or near term on pregnancy outcomes in high-resource settings. METHODS: The MEDLINE, Embase, CINAHL, Cochrane Library, MIDIRS, and SweMED+ databases were searched for relevant articles published through April 2019, with no limitation on publication date. Clinical trials comparing the effects of ECV at ≥36 weeks, with or without tocolysis, with that of no ECV, conducted in northern, western, and central Europe, the USA, Canada, Australia, and New Zealand were eligible for inclusion. RESULTS: Nine articles reporting on 184704 breech pregnancies were included. Pooled data showed that ECV attempts reduced the failure to achieve vaginal cephalic birth (risk ratio, RR=0.56; 95% CI: 0.45-0.71), caesarean section performance (RR=0.57; 95% CI: 0.50-0.64), and non-cephalic presentation at birth (RR=0.45; 95% CI: 0.29-0.68) compared with no ECV. ECV attempts also increased the incidence of Apgar score <7 at 5 minutes (RR=1.29; 95% CI: 1.10-1.52). CONCLUSIONS: Women for whom ECV is attempted at or near term are at reduced risk of caesarean section, non-cephalic presentation at term, and failure to achieve vaginal cephalic birth. Compared with no ECV, attempted ECV was also associated with a slightly increased risk of a low Apgar score at 5 minutes. The evidence is limited by the scarcity of high-quality research and the presence of risks of bias.
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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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".