Newborn Resuscitation Training Programmes Reduce Early Neonatal Mortality
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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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".