Continuous flow insufflation of oxygen compared with manual ventilation during out-of-hospital cardiac arrest: A survey of the paramedics
Bibliographic record
Abstract
Introduction:In 2018, a continuous flow insufflation of oxygen (CFIO) device (b-card™, Vygon (USA)) placed on a supraglottic airway (SGA) became the standard of care to ventilate patients during adult out-of-hospital cardiac arrest (OHCA) care in Quebec–Capitale-Nationale region, Canada. This study aims to assess the paramedics’ perception as well as the disadvantages and the benefits relative to the use of CFIO during OHCA management.Methods:An invitation to complete an online survey (Survey Monkey™) was sent to all 560 paramedics who are working in our region. The survey included 22 questions of which 9 aimed to compare the traditional manual ventilation with a bag to the CFIO using a 5-point Likert-type scale.Results:A total of 244 paramedics completed the survey, of which 189 (77.5%) had used the CFIO device during an OHCA at least once. Most respondents felt that the intervention was faster (70.2%) and easier (86.5%) with the CFIO device compared with manual ventilation. CFIO was also associated with perceived increased patient safety (64.4%) as well as paramedic safety during the evacuation (88.9%) and the ambulance transport (88.9%). Paramedics reported that physical (48.1%) and cognitive (52.9%) fatigue were also improved with CFIO. The main reported barriers were the bending of the external SGA tube and the loss of capnography values.Conclusion:The use of CFIO during adult OHCA care allows a simplified approach and was perceived as safer for the patient and the paramedics compared with manual ventilation. Its impact on patient-centred outcomes needs to be assessed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".