The Airway App: exploring the role of smartphone technology to capture emergency front‐of‐neck airway experiences internationally
Bibliographic record
Abstract
In this exploratory study we describe the utility of smartphone technology for anonymous retrospective observational data collection of emergency front-of-neck airway management. The medical community continues to debate the optimal technique for emergency front-of-neck airway management. Although individual clinicians infrequently perform this procedure, hundreds are performed annually worldwide. Ubiquitous smartphone technology and internet connectivity have created the opportunity to collect these data. We created the 'Airway App', a smartphone application to capture the experiences of healthcare providers involved in emergency front-of-neck airway procedures. In the first 18-month period, 104 emergency front-of-neck airway management reports were received; 99 (95%) were internally valid and unique from 21 countries. Eighty-one (82%) were performed by non-surgeons and 63 (64%) were 'cannot intubate, cannot oxygenate' emergencies. Overall first-attempt success varied by technique; 45 scalpel-bougie cricothyroidotomy (37 first-attempt success), 25 surgical cricothyroidotomy (15 first-attempt success), eight cannula cricothyroidotomy (five first-attempt success), six wire-guided cricothyroidotomy (three first-attempt success) and 15 tracheostomy reports (11 first-attempt success). The most commonly reported positive human factors were good communication, good teamwork and/or skilled personnel. The most commonly reported negative human factors were fixation on multiple tracheal intubation attempts, delay in initiating emergency front-of-neck airway and/or the failure to plan for failure. Due to the anonymous nature of reporting, reports are open to recollection bias and spurious reporting. We conclude collection of data using a smartphone application is feasible and has the potential to expand our knowledge of emergency front-of-neck airway management.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".