What do the numbers say? - Introduction of the WHO ICD-PM classification and fetuses-at risk approach in perinatal audit, South India
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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