Prevalence and predictors of uterine rupture among Ethiopian women: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Uterine rupture has a significant public health importance, contributing to 13% of maternal mortality and 74%-92% of perinatal mortality in Sub-Saharan Africa, and 36% of maternal mortality in Ethiopia. The prevalence and predictors of uterine rupture were highly variable and inconclusive across studies in the country. Therefore, this systematic review and meta-analysis aimed to estimate the pooled prevalence and predictor of uterine rupture in Ethiopia. METHODS: This systematic review and meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2009 checklist. PubMed, Cochrane Library, Google Scholar, and African Journals Online databases were searched. The Newcastle- Ottawa quality assessment tool was used for critical appraisal. I2 statistic and Egger's tests were used to assess the heterogeneity and publication bias, respectively. The random-effects model was used to estimate the pooled prevalence and odds ratios with a 95% confidence interval. RESULTS: Sixteen studies were included, with a total of 91,784 women in the meta-analysis. The pooled prevalence of uterine rupture was 2% (95% CI: 1.99, 3.01). The highest prevalence was observed in the Amhara regional state (5%) and the lowest was in Tigray region (1%). Previous cesarean delivery (OR = 9.95, 95% CI: 3.09, 32.0), lack of antenatal care visit (OR = 8.40, 95% CI: 4.5, 15.7), rural residence (OR = 4.75, 95% CI: 1.17, 19.3), grand multiparity (OR = 4.49, 95% CI: 2.83, 7.11) and obstructed labor (OR = 6.75, 95%CI: 1.92, 23.8) were predictors of uterine rupture. CONCLUSION: Uterine rupture is still high in Ethiopia. Therefore, proper auditing on the appropriateness of cesarean section and proper labor monitoring, improving antenatal care visit, and birth preparedness and complication readiness plan are needed. Moreover, early referral and family planning utilization are the recommended interventions to reduce the burden of uterine rupture among Ethiopia women.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.043 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".