Real-time ventilation quality feedback devices efficacy in out-of-hospital cardiac arrest: a scoping review
Bibliographic record
Abstract
Background: New devices are now available to provide real-time feedback on ventilation for basic life support providers responding to out-of-hospital cardiac arrest (OHCA). This scoping review, conducted as part of the evidence review for the International Liaison Committee on Resuscitation, aimed to examine the extent of evidence examining ventilation feedback devices and to identify research gaps regarding these devices. Methods: This scoping review was conducted using Arksey and O'Malley's framework and reported according to PRISMA-ScR guidelines. Medline, EMBASE and Cochrane were searched from database inception to March 13th, 2025. Studies examining real-time ventilation quality feedback in humans and manikins of any design were included. Ventilation feedback devices were defined as any device that can provide information on the delivery of each insufflation (including insufflation and/or exsufflation measured volume as well as rate) and to guide the ventilation through real-time feedback. Results: We screened 794 titles, with 17 studies (including 4 conference abstracts) included: one randomised trial (RCT), one before-after prospective studies, two observational studies, one case series and 12 simulation studies. Only three simulation studies assessed a pediatric scenario. The RCT reported improved early outcomes (unadjusted return of spontaneous circulation and 30-hour survival) with real-time feedback, but no difference at hospital discharge. Two observational studies also found no change in patient outcomes, but noted improved ventilation rate and insufflation volumes. Most simulation studies showed improvements in ventilation parameters. Conclusion: Real-time feedback devices seem to improve ventilations, but we found insufficient evidence of their effect on clinical outcomes to merit a systematic review at this time. Rigorous evaluation of the clinical efficacy and effectiveness of these devices is 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 | 0.019 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 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".