A review of approaches to optimise chest compressions in the resuscitation of asphyxiated newborns
Bibliographic record
Abstract
OBJECTIVE: Provision of chest compressions (CCs) and/or medications in the delivery room is associated with poor outcomes. Based on the physiology of perinatal asphyxia, we aimed to provide an overview of current recommendations and explore potential determinants of effective neonatal cardiopulmonary resuscitation (CPR): balancing ventilations and CC, CC rate, depth, full chest recoil, CC technique and adrenaline. DESIGN: A search in the databases MEDLINE (Ovid) and EMBASE until 10 April 2015. SETTING: Delivery room. PATIENTS: Asphyxiated newborn infants. INTERVENTIONS: CCs. MAIN OUTCOME MEASURES: Haemodynamics, recovery and survival. RESULTS: Current evidence is derived from mathematical models, manikin and animal studies, and small case series. No randomised clinical trials examining neonatal CC have been performed. There is no evidence to refute a CC to ventilation (C:V) ratio of 3:1. Raising the intrathoracic pressure, for example, by superimposing a sustained inflation on uninterrupted CC, and a CC rate >120/min may be beneficial. The optimal neonatal CC depth is unknown, but factors influencing depth and consistency include the C:V ratio. Incomplete chest wall recoil can cause less negative intrathoracic pressure between CC and reduced CPR effectiveness. CC should be performed with the two-thumb method over the lower third of the sternum. The optimal dose, route and timing of adrenaline administration remain to be determined. CONCLUSIONS: Successful CPR requires the delivery of high-quality CC, encompassing optimal (A) C:V ratio (B) rate, (C) depth, (D) chest recoil between CC, (E) technique and (F) adrenaline dosage. More animal studies with high translational value and randomised clinical trials are needed.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".