Use of the International Classification of Diseases to Perinatal Mortality (ICD-PM) with verbal autopsy to determine the causes of stillbirths and neonatal deaths in rural Cambodia: a population-based, prospective, cohort study
Bibliographic record
Abstract
Abstract Background Perinatal mortality remains a significant global health challenge, particularly in low- and middle-income countries (LMICs). Accurate cause-of-death data are essential to inform effective interventions but is often scarce. This study aimed to identify causes of stillbirths and neonatal deaths in rural Cambodia using verbal autopsy (VA) and the WHO International Classification of Diseases to Perinatal Mortality (ICD-PM). Methods A four-year prospective study (2018-2022) in Preah Vihear province, Cambodia, established a community health worker-based pregnancy surveillance system. Verbal autopsy was conducted on stillbirths and neonatal deaths, with dual physician analysis to interpret VA data. To classify causes of death, ICD-PM was applied with adaptations made for stillbirths with unknown timing of death. Results A total of 522 deaths (229 stillbirths, 293 neonatal deaths) were recorded, and 79.1% (413) had a VA. Applying ICD-PM, primary causes of death were identified for 36.6% of stillbirths and 95.0% of neonatal deaths. The leading cause of death was hypoxia for intrapartum stillbirths (78.3%), low birth weight and prematurity for early neonatal deaths (40.9%), and infection for late neonatal deaths (51.4%). Complications during labour and delivery were the leading maternal contributing condition for intrapartum stillbirths (63.3%) and early neonatal deaths (42.4%). Unknown timing of death was assigned to 12.0% of stillbirths. Conclusion Application of ICD-PM with VA-derived data provides valuable insights into causes of stillbirths and neonatal deaths. However, adaptations are necessary to address ICD-PM’s limitations, particularly to classify unknown timing of death. Our findings can contribute to global efforts to improve the reporting of perinatal mortality data.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".