Newborn Resuscitation Training Programmes Reduce Early Neonatal Mortality
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Substantial health care resources are expended on standardised formal neonatal resuscitation training (SFNRT) programmes, but their effectiveness has not been proven. OBJECTIVES: To determine whether SFNRT programmes reduce neonatal mortality and morbidity, improve acquisition and retention of knowledge and skills, or change teamwork and resuscitation behaviour. METHODS: We searched CENTRAL, MEDLINE, PREMEDLINE, EMBASE, CINAHL, Web of Science and the Oxford Database of Perinatal Trials, ongoing trials and conference proceedings in April 2015, and included randomised or quasi-randomised trials that reported at least one of our specified outcomes. RESULTS: SFNRT in low- and middle-income countries decreased early neonatal mortality [risk ratio (RR) 0.85 (95% CI 0.75-0.96)]; the number needed to treat for benefit [227 (95% CI 122-1,667; 3 studies, 66,162 participants, moderate-quality evidence)], and 28-day mortality [RR 0.55 (95% CI 0.33-0.91); 1 study, 3,355 participants, low-quality evidence]. Decreasing trends were noted for late neonatal mortality [RR 0.47 (95% CI 0.20-1.11)] and perinatal mortality [RR 0.94 (95% CI 0.87-1.00)], but there were no differences in fresh stillbirths [RR 1.05 (95% CI 0.93-1.20)]. Teamwork training with simulation increased the frequency of teamwork behaviour [mean difference (MD) 2.41 (95% CI 1.72-3.11)] and decreased resuscitation duration [MD -149.54 (95% CI -214.73 to -84.34); low-quality evidence, 2 studies, 130 participants]. CONCLUSIONS: SFNRT in low- and middle-income countries reduces early neonatal mortality, but its effects on birth asphyxia and neurodevelopmental outcomes remain uncertain. Follow-up studies suggest normal neurodevelopment in resuscitation survivors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it