PREHOSPITAL AIRWAY MANAGEMENT: A SYSTEMATIC REVIEW OF TECHNIQUES AND OUTCOMES
Bibliographic record
Abstract
Objective: This systematic review and meta-analysis aims to synthesize the current evidence on the efficacy, safety, and patient outcomes associated with different prehospital airway management techniques, including bag-valve-mask (BVM) ventilation, endotracheal intubation (ETI), and supraglottic airways (SGAs). Methods: A systematic search of MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Web of Science, and Scopus was conducted for randomized controlled trials and observational studies from inception to the present. Studies comparing advanced airway management (ETI or SGA) to BVM or to each other in prehospital patients were included. Primary outcomes were survival to hospital discharge and survival with a favourable neurological outcome. Study quality was assessed using the Cochrane RoB 2 tool and the Newcastle-Ottawa Scale. Results: Twenty-three studies with a total of over 1.2 million patients were included. Meta-analysis revealed no significant difference in survival to hospital discharge between advanced airways (ETI or SGA) and BVM ventilation (RR 1.08, 95% CI 0.92 to 1.27). However, the use of advanced airways was associated with a statistically significant reduction in the likelihood of a favourable neurological outcome compared to BVM (RR 0.86, 95% CI 0.78 to 0.95). SGAs demonstrated a higher first-pass success rate (92%) and shorter placement time than ETI (78%). Conclusion: In patients with out-of-hospital cardiac arrest, advanced airway management (ETI or SGA) is associated with worse neurological outcomes compared to BVM ventilation, despite no difference in survival. The findings advocate for a paradigm shift towards optimizing BVM ventilation as a first-line strategy. When an advanced airway is necessary, SGAs may be a more practical option than ETI due to higher success rates and faster placement. Keywords: Prehospital Emergency Care, Airway Management, Cardiopulmonary Resuscitation, Out-of-Hospital Cardiac Arrest, Emergency Medical Services.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.026 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".