Bibliographic record
Abstract
Objective: The purpose of the research reported in this paper was to investigate, using data gathered from simulated Out of Hospital Cardiac Arrest (OHCA) exercises, the impact on the quality of chest compressions when introducing a King Laryngeal-Tube (KLT) airway and then employing the LUCAS mechanical CPR device or employing the LUCAS and then securing the KLT airway.These findings may also provide EMS practitioners with evidence-based guidance on how to better employ the LUCAS in an OHCA.Methods: Student paramedic 2-person crews were assigned a simulated call for a "58 y/o patient, not conscious, not-breathing".On entering the scene, the crew finds an adult manikin supine, on the floor.The crew managed the initial stages of a simulated adult OHCA VF simulation using a simple, well-practiced BLS drill, a LUCAS, and a Supra-Glottic-Airway (SGA) while minimising interruptions to chest compressions.Results: The primary outcome measure was the length of time that no chest compressions were taking place in each simulated scenario.A significant statistical difference was found between the LUCAS-first condition, with a mean time off the chest of 4.63 s and the Airway-first condition, which had 5.25 s, z (-8.509) with critical value 1.96.Conclusion: The data showed the difference between a two-stage deployment of the LUCAS before and after the KLT airway was statistically significant in favour of the LUCAS first group, however, it was considered not clinically significant as the difference in the time off the chest was less than 10 s between the two conditions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.777 | 0.542 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".