Airway management by first responders when using a bag-valve device and two oxygen-driven resuscitators in 104 patients
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: To evaluate the capability of first responders to ensure an airway and ventilate the lungs of a patient employing a bag-valve device and two oxygen-driven resuscitators. METHODS: Prospective, controlled, blinded, single-centre clinical trial using a bag-valve device and one of two FR-300 devices, with 20 cmH2O working pressure, flows of 24 and 30 L min(-1). One-hundred-and-four patients were analysed. Induction of anaesthesia was followed by ventilation of the lungs with a bag-valve device and an Oxylator (CPR Medical Devices Corp., Markham, Ontario, Canada) in manual and automatic modes. Each series was repeated twice by a fireman first responder using a hand-held mask to seal the airway, once under anaesthesia and then again under anaesthesia with muscle relaxation. RESULTS: Patients' mean age 49 +/- 17 yr; 47% male, 48-132 kg. Only 29% had optimal facial and airway physiognomy. Airway management was significantly poorer when the bag-valve device was used than with either Oxylator mode (P < 0.0001); 23% of cases were not manageable with the bag-valve device. Gastric insufflation was markedly less with the Oxylator (P < 0.02). CONCLUSIONS: The use of an oxygen-driven device improves the ability of first responders to secure an airway and reduce gastric insufflation, even when distracted. Oxylators perform significantly better (P < 0.0001) than the bag-valve device.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".