Caesarean Sections in a National Referral Hospital in Addis Ababa, Ethiopia: Trends, Predictors and Outcomes [27D]
Bibliographic record
Abstract
INTRODUCTION: The objective of this study was to analyze Caesarean section (CS) rate trends and maternal and perinatal outcomes in an Ethiopian hospital using Robson criteria groups and subdividing further based on urgency. METHODS: This was a retrospective study of deliveries at a national referral hospital between 2002/01/01 and 2006/13/05 (Ethiopian calendar) using cluster sampling of deliveries of gestational age ≥28 weeks. (N=4,816). Women were categorized into 10 Robson groups (RGs) with CS deliveries further classified by urgency. Perinatal mortality and neonatal distress rates were used to characterize perinatal outcomes. Maternal morbidity rate was used to characterize maternal outcomes. RESULTS: The total CS rate rose from 24.5% to 32.8% (p=0.001). The only RG showing a significant change in CS rate was RG1 (15.9% - 24.1%; p= 0.02), which experienced a 51% relative increase. RG1 contributed most to the total CS rate in all 5 years (7.8%), followed by RG3 and RG5. The “Scheduled” urgency subgroup was largest within the majority of RGs. Neonatal outcomes did not significantly change over time. Overall maternal morbidity rate increased from 3.5% to 4.1% (p=0.02). CONCLUSION: The threshold for medically indicated CS has decreased in this publicly funded hospital, especially in low-risk women. We did not see increased complications, but CS without medical indication can result in harm and strain on already limited resources. As primary CS rates increase, more women will require repeat CS. Evidence-based interventions to reduce primary and repeat CS should be studied and implemented at this Ethiopian hospital.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".