Vaginal birth after cesarean section and its associated factors in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
The prevalence of cesarean sections is rising rapidly and is becoming a global issue. Vaginal birth after a cesarean section is one of the safest strategies that can be used to decrease the cesarean section rate. Different fragmented primary studies were done on the success rate of vaginal birth after cesarean section and its associated factors in Ethiopia. However, the findings were controversial and inconclusive. Therefore, this meta-analysis was intended to estimate the pooled success rate of vaginal birth after cesarean section and its associated factors in Ethiopia. Pertinent studies were searched in PubMed, Google Scholar, ScienceDirect, direct open-access journals, and Ethiopian universities' institutional repositories. The data were analyzed using Stata 17. The Newcastle-Ottawa quality assessment tool was used to assess the quality of the studies. I squared statistics and Egger's regression tests were used to assess heterogeneity and publication bias, respectively. A random effects model was selected to estimate the pooled success rate of vaginal birth after cesarean section and its associated factors. The PROSPERO registration number for this review is CRD42023413715. A total of 10 studies were included. The pooled success rate of vaginal birth after a cesarean section was found to be 48.42%. Age less than 30 years (pooled odds ratio (OR) 3.75, 95% CI 1.92, 7.33), previous history of vaginal birth (OR 3.65, 95% CI 2.64, 504), ruptured amniotic membrane at admission (OR 2.87, 95% CI 1.94, 4.26), 4 cm or more cervical dilatation at admission (OR 4, 95% CI 2.33, 6.8), a low station at admission (OR 5.07, 95% CI 2.08, 12.34), and no history of stillbirth (OR 4.93, 95% CI 1.82, 13.36) were significantly associated with successful vaginal birth after cesarean section. In conclusion, the pooled success rate of vaginal birth after a cesarean section was low in Ethiopia. Therefore, the Ministry of Health should consider those identified factors and revise the management guidelines and eligibility criteria for a trial of labor after a cesarean section.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.028 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".