Chest compressions in newborn infants: a scoping review
Bibliographic record
Abstract
AIM: The International Liaison Committee on Resuscitation Neonatal Life Support Task Force undertook a scoping review of the literature to identify evidence relating to neonatal cardiopulmonary resuscitation. METHODS: MEDLINE complete, EMBASE and Cochrane database of Systematic reviews were searched from inception to November 2021. Two authors screened titles and abstracts and full text reviewed. Studies were eligible for inclusion if they were peer-reviewed and assessed one of five aspects of chest compression in the newborn infant including: (1) heart rate thresholds to start chest compressions (CC), (2) compression to ventilation ratio (C:V ratio), (3) CC technique, (4) oxygen use during CC and 5) feedback devices to optimise CC. RESULTS: Seventy-four studies were included (n=46 simulation, n=24 animal and n=4 clinical studies); 22/74 were related to compression to ventilation ratios, 29/74 examined optimal technique to perform CC, 7/74 examined oxygen delivery and 15/74 described feedback devices during neonatal CC. CONCLUSION: There were very few clinical studies and mostly manikin and animal studies. The findings either reinforced or were insufficient to change previous recommendations which included to start CC if heart rate remains <60/min despite adequate ventilation, using a 3:1 C:V ratio, the two-thumb encircling technique and 100% oxygen during CC.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".