Factors associated with successful vaginal birth after a cesarean section: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Evidence for the relationship between maternal and perinatal factors and the success of vaginal birth after cesarean section (VBAC) is conflicting. We aimed to systematically analyze published data on maternal and fetal factors for successful VBAC. METHODS: A comprehensive search of Medline, Embase, and the Cumulative Index to Nursing and Allied Health Literature, from each database's inception to March 16, 2018. Observational studies, identifying women with a trial of labor after one previous low-transverse cesarean section were included. Two reviewers independently abstracted the data. Meta-analysis was performed using the random-effects model. Risk of bias was assessed by the Newcastle-Ottawa Scale. RESULTS: We included 94 eligible observational studies (239,006 pregnant women with 163,502 VBAC). Factors were associated with successful VBAC with the following odds ratios (OR;95%CI): age (0.92;0.86-0.98), obesity (0.50;0.39-0.64), diabetes (0.50;0.42-0.60), hypertensive disorders complicating pregnancy (HDCP) (0.54;0.44-0.67), Bishop score (3.77;2.17-6.53), labor induction (0.58;0.50-0.67), macrosomia (0.56;0.50-0.64), white race (1.39;1.26-1.54), previous vaginal birth before cesarean section (3.14;2.62-3.77), previous VBAC (4.71;4.33-5.12), the indications for the previous cesarean section (cephalopelvic disproportion (0.54;0.36-0.80), dystocia or failure to progress (0.54;0.41-0.70), failed induction (0.56;0.37-0.85), and fetal malpresentation (1.66;1.38-2.01)). Adjusted ORs were similar. CONCLUSIONS: Diabetes, HDCP, Bishop score, labor induction, macrosomia, age, obesity, previous vaginal birth, and the indications for the previous CS should be considered as the factors affecting the success of VBAC.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.006 | 0.008 |
| 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) | 0.003 | 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".