Evaluation of ventilatory parameters reporting in large animal models of cardiac arrest: a scoping review
Bibliographic record
Abstract
Background Ventilation is a critical determinant of cardiopulmonary resuscitation efficiency. Our goal was to evaluate how ventilatory parameters are reported during cardiopulmonary resuscitation in large animal models of cardiac arrest. Methods A scoping review was conducted following the PRISMA-ScR guidelines, including studies referenced in Pubmed over the last decade (January 1st, 2015, to July 30th, 2025). The review followed the PCC approach: (P) population: large animal models of cardiac arrest; (C) concept: ventilatory settings and parameters during CPR; (C) context: studies aiming at describing or evaluating mechanical or manual ventilation during CPR in experimental conditions. The reporting of the animal characteristics, ventilatory settings and monitored parameters were extracted and analyzed descriptively. Results We identified 111 relevant publications. Most of them used porcine models (79%), with ventricular fibrillation being the most common method of cardiac arrest induction (59%). Mechanical ventilation was predominant (75%), with volume and pressure-controlled modes nearly equally represented. The reporting of critical ventilatory settings was inconsistent, with a percentage of appropriate reporting as follows: respiratory rate (88%), fraction of inspired oxygen (83%), positive end-expiratory pressure (49%), tidal volume (83%, among studies with volume-controlled ventilation), peak inspiratory pressure (92%, among studies with pressure-controlled ventilation) and inspiratory to expiratory ratio (17%, among all studies with mechanical ventilation). Reporting of measured ventilatory parameters during CPR was also limited with, e.g., EtCO 2 reported in 41% of the studies and arterial blood gases sampled and reported in 50% of the studies. Conclusions This scoping review evidenced substantial variability and frequent omissions in the reporting of ventilatory settings and monitoring in large animal CPR studies. Updated recommendations could be useful to provide specific guidelines of reporting in the field.
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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 | 0.049 | 0.177 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.029 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".