Abstract P18: Automated High Frequency Ventilation During Experimental CPR: A New Approach to Resuscitation
Bibliographic record
Abstract
Purpose: Experimental and clinical studies suggest manual over-ventilation during cardiopulmonary resuscitation ( CPR ) is common and reduces maximum obtainable ‘cardiac output’ from chest compressions. Automated ventilation during CPR has not been previously tested. Methods: This study compared hemodynamic (using solid state Millar transducers), gas exchange, and ventilation parameters during experimental CPR using 100% O 2 and manual bag ventilation (6 – 8 breaths/min) compared to automated ventilation using a compressed gas powered, non-electronic, pressure limited, pressure and flow triggered device (Oxylator, CPR Medical Devices) set at maximum inspiratory pressure of 12 mmHg, PEEP = 1.5 mmHg, max flow 30 L/min. The device automatically delivers inhalations when airway pressure falls to 1.5 mmHg and stops when pressure reaches 12 mmHg, delivering 1 breath for every decompression during manual chest compression. Twelve pigs underwent 5 min of electrically induced, untreated ventricular fibrillation (VF). They were randomly assigned to CPR using manual or automated ventilations (n = 6 each), together with continuous manual chest compressions at 95 – 110/min, 3 – 5 cm depth. Carotid artery flow was measured using a Doppler flow probe. All measures were made after 2 min of CPR. Results: Ventilatory and hemodynamic parameters for manual vs. automated ventilation during CPR Conclusion: Automated high frequency, low pressure ventilation during experimental CPR is associated with hemodynamics and gas exchange similar to that achieved with guideline recommended manual ventilation and avoids the risk of over-ventilation and barotrauma.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".