Applying the WHO ICD-PM classification system to stillbirths in a major referral Centre in Northeast Nigeria: a retrospective analysis from 2010-2018
Bibliographic record
Abstract
BACKGROUND: Lack of a unified and comparable classification system to unravel the underlying causes of stillbirth hampers the development and implementation of targeted interventions to reduce the unacceptably high stillbirth rates (SBR) in sub-Saharan Africa. Our aim was to track the SBR and the predominant maternal and fetal causes of stillbirths using the WHO ICD-PM Classification system. METHODS: This was a retrospective observational study in a major referral centre in northeast Nigeria between 2010 and 2018. Specialist Obstetricians and Gynaecologists assigned causes of stillbirths after an extensive audit of available stillbirths' records. Cause of death was assigned via consensus using the ICD-PM classification system. RESULTS: There were 21,462 births between 1 January 2010 and 31 December 2018 in our study setting; of these, 1177 culminated in stillbirths with a total hospital SBR of 55 per 1000 births (95% CI: 52, 58). There were two peaks of stillbirths in 2012 [62 per 1000 births (95% CI: 53, 71)], and 2015 [65 per 1000 births (95% CI, 55, 76)]. Antepartum and intrapartum stillbirths were almost equally prevalent (48% vs 52%). Maternal medical and surgical conditions (M4) were the commonest (69.3%) cause of antepartum stillbirths while complications of placenta, cord and membranes (M3) accounted for the majority (45.8%) of intrapartum stillbirths and the trends were similar between 2010 and 2018. Antepartum and intrapartum fetal causes of stillbirths were mainly due to prematurity which is a disorder of fetal growth (A5 and I6). CONCLUSIONS: Most causes of stillbirths in our setting are due to preventable causes and the trends have remained unabated between 2010 and 2018. Progress toward global SBR targets are off-track, requiring more interventions to halt and reduce the high SBR.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".