Investigating the Success Rate of Vaginal Delivery After Cesarean Section and Its Associated Factors in Afghan Women: Insights from a Maternity Hospital in Kabul
Bibliographic record
Abstract
Background: Vaginal Birth After Cesarean delivery (VBAC) is widely regarded as one of the most effective methods to reduce unnecessary Cesarean section and their complications. Despite its proven benefits, data on the success rate of VBAC and the associated factors remain scarce in Afghanistan. This study aimed to address this gap by determining the VBAC success rate and identifying its associated factors in a maternity hospital in Kabul, Afghanistan. Methods: < 0.05. Logistic regression analysis was performed to identify independent predictors of VBAC, with VBAC as the outcome variable and multiple predictors included in the model. Results: Among the 567 women included in the study, 449 (79.2%) had a successful VBAC. Factors associated with successful VBAC and their adjusted odds ratio (95% CI) were lower gestational age: 1.25 (1.05-1.48), absence of gestational hypertension: 2.71 (1.26-5.85), cervical dilation of > 4 cm on admission: 2.77 (1.38-5.55), effacement of ≥ 50% on admission: 2.13 (1.04-4.35), and absence of fetal distress: 7.35 (4.29-12.6). Conclusion: The rate of successful VBAC observed in this study is at a high level (79.2%). This study is the first study to determine the VBAC success rate and its associated factors in Afghanistan. Further research is needed to validate these findings and explore additional factors influencing VBAC success.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".