What do the numbers say? - Introduction of the WHO ICD-PM classification and fetuses-at risk approach in perinatal audit, South India
Bibliographic record
Abstract
Objective: In India, despite a reduction in perinatal mortality rate from 2014 to 2019, still birth rate is still the same at the national average of 4/1000 live births. As yet there is no nation-wide audit in India except for facility based audits. Hence the need for a simplified yet effective audit process exists. The aim of this study was to perform a qualitative perinatal audit and devise methods for future audits. Methods: We conducted a one year audit for all perinatal deaths using WHO ICD PM and 3-delay classification. Gestational age (GA) specific mortality was calculated for significant underlying factors using fetuses-at risk approach. Results: We recorded a perinatal mortality rate of 6.1/1000 births among booked cases and 21.32/1000 births among referred cases. Fetal growth restriction was the most common antenatal condition, accounting to 33.3% of antepartum deaths. Prematurity accounted to 52% of neonatal deaths. Phase 2 delay with delayed referrals in severe pre-eclampsia and Phase 1 delay with late visit (> 24 h) to hospital after experiencing absent fetal movements were the most common identifiable delays. Hypertension stood out to be the single most common risk-factor. GA specific mortalities, calculated using fetuses-at risk approach, show a peak mortality rate at 30 weeks, 37 weeks and 38 weeks in pregnancies with early-onset preeclampsia, severe fetal growth restriction and medically treated gestational diabetes respectively. Conclusion: The audit identified significant contributing factors to the mortality. ICD-PM and 3-delay classification was simpler and easier to apply with wide areas of opportunities for secondary analysis.
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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 | 0.021 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".