Disclosing suboptimal indications for emergency caesarean sections due to fetal distress and prolonged labor: a multicenter cross-sectional study at 12 public hospitals in Nepal
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Bibliographic record
Abstract
BACKGROUND: Global caesarean section (CS) rates have raised concern of a potential overuse of the procedure in both high- and low-resource settings. We sought to assess management and outcomes of deliveries with emergency CSs due to fetal distress and prolonged labor at 12 public hospitals in Nepal and determine factors associated with suboptimal CS indications. METHODS: We conducted a cross-sectional study on all deliveries between the 14th of April 2017 and the 17th of October 2018 at 12 public hospitals in Nepal and included all emergency CSs due to fetal distress and prolonged labor. Analysis was conducted using Pearson chi-square test and bivariate and multivariate logistic regression. RESULTS: The total cohort included 104,322 deliveries of which 18,964 (18%) were CSs (13,095 [13%] emergency CSs and 5230 [5.0%] elective CSs). We identified 1806 emergency CSs due to fetal distress and 1322 emergency CSs due to prolonged labor. Among CSs due to fetal distress, only 36% had fetal heart rate monitoring performed according to protocol, and among CSs due to prolonged labor, the partograph was completely filled in only 8.6%. Gestational age < 37 weeks and birth weight < 2500 g were associated with more suboptimal CS indications due to fetal distress (adjusted odds ratio [aOR] 1.4, 95% confidence interval [CI] 1.1-1.8 and aOR 1.7, 95% CI 1.3-2.2 respectively) than those with gestational age > 37 weeks and birth weight > 2500 g. We found no association between suboptimal CS indications and maternal ethnicity or education level. CONCLUSIONS: As fetal heart rate monitoring and partograph are fundamental to diagnose fetal distress and prolonged labor, the inappropriate monitoring proceeding CS decisions disclosed in our study indicate that CSs were performed on suboptimal indications. We call for improved quality of intrapartum monitoring, enhanced documentation in medical records, and structured auditing of CS indications in order to curb the potentially harmful CS trend.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it