The Impact of Location and Asset Type on the Success of Advanced Airway Management in a Critical Care Transport Environment
Bibliographic record
Abstract
OBJECTIVE: Advanced airway management (AAM) is a critical component of prehospital critical care. Airway management in flight can be more challenging because of spatial, ergonomic, and environmental factors. This study examines the frequency of in-flight intubation (IFI), first-pass success (FPS) rates, and definitive airway sans hypoxia/hypotension on first attempt (DASH-1A) across different locations of airway management. METHODS: We conducted a retrospective database analysis of all patients transported between January 2016 and July 2021 who received AAM from a single air medical service. Patient records were reviewed for location of intubation, patient characteristics, and FPS and DASH-1A rates. The primary outcome was the frequency of IFI. The secondary outcomes included FPS and DASH-1A rates by location and type of transport asset. RESULTS: During the study period, 473 patients required AAM. Three percent (15/473) of patients were intubated in an in-flight setting, 28% (130/473) were intubated on scene, and 70% (328/473) were intubated in a health care facility. The primary reason for IFI was unanticipated cardiac arrest or clinical deterioration. The overall FPS rate was 69% (328/473), and the DASH-1A rate was 49% (194/399). Based on the location of AAM, the FPS and DASH-1A rates were the lowest for on-scene intubations (56% [74/130] and 27% [20/74], respectively). Most of the on-scene AAM took place with rotor wing flight crews. CONCLUSION: Airway management occurs infrequently in an in-flight setting and is necessary because of patient deterioration or cardiac arrest. Based on our results, we identified opportunities for targeted AAM quality improvement and clinical governance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".