Stillbirth and Newborn Mortality in India After Helping Babies Breathe Training
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Bibliographic record
Abstract
OBJECTIVE: This study evaluated the effectiveness of Helping Babies Breathe (HBB) newborn care and resuscitation training for birth attendants in reducing stillbirth (SB), and predischarge and neonatal mortality (NMR). India contributes to a large proportion of the worlds annual 3.1 million neonatal deaths and 2.6 million SBs. METHODS: This prospective study included 4187 births at >28 weeks' gestation before and 5411 births after HBB training in Karnataka. A total of 599 birth attendants from rural primary health centers and district and urban hospitals received HBB training developed by the American Academy of Pediatrics, using a train-the-trainer cascade. Pre-post written trainee knowledge, posttraining provider performance and skills, SB, predischarge mortality, and NMR before and after HBB training were assessed by using χ(2) and t-tests for categorical and continuous variables, respectively. Backward stepwise logistic regression analysis adjusted for potential confounding. RESULTS: Provider knowledge and performance systematically improved with HBB training. HBB training reduced resuscitation but increased assisted bag and mask ventilation incidence. SB declined from 3.0% to 2.3% (odds ratio [OR] 0.76, 95% confidence interval [CI] 0.59-0.98) and fresh SB from 1.7% to 0.9% (OR 0.54, 95% CI 0.37-0.78) after HBB training. Predischarge mortality was 0.1% in both periods. NMR was 1.8% before and 1.9% after HBB training (OR 1.09, 95% CI 0.80-1.47, P = .59) but unknown status at 28 days was 2% greater after HBB training (P = .007). CONCLUSIONS: HBB training reduced SB without increasing NMR, indicating that resuscitated infants survived the neonatal period. Monitoring and community-based assessment are recommended.
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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