To Evaluate Quality of Cardiopulmonary Resuscitation (CPR) Performed At Scene and During Transport After Out-of-Hospital Cardiac Arrest: A Crossover Trial
Bibliographic record
Abstract
Background: High-quality cardiopulmonary resuscitation (CPR) is essential to improve survival rates in out-of-hospital cardiac arrest. Previous research suggests that the quality of CPR may decline during ambulance transport. Aims: To evaluate the quality of CPR performed at the scene and during transport after out-of-hospital cardiac arrest. Methods: A randomised, controlled, crossover trial was conducted to compare the performance of CPR on a manikin, both at the scene and in a moving ambulance. CPR quality was measured using a QCPR system, capturing parameters including compression depth, rate, recoil, and ventilation sufficiency. Participants alternated between the two scenarios after a one-hour recovery period. Results: 18 participants were enrolled, with a mean score for CPR quality of 52.94% in the ambulance and 53.25% at the scene. A t-test revealed no statistically significant difference (t(34) = -0.033, p = 0.97) in scores; furthermore, we observed no statistically significant differences in relation to the following parameters: flow fraction, cycles performed, total number of compressions, compression score, percentage of compressions performed with proper release, percentage of compressions performed with adequate depth, total number of ventilations, and ventilation score (p>0.05). Conclusion: The quality of CPR provided at the scene and in the ambulance is comparable, suggesting that competent CPR providers can deliver high-quality care in both settings.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".