Incidence and predictors of critical events during urgent air-medical transport
Bibliographic record
Abstract
BACKGROUND: Little is known about the risks of urgent air-medical transport used in regionalized health care systems. We sought to determine the incidence of in-transit critical events and identify factors associated with these events. METHODS: We conducted a population-based, retrospective cohort study using clinical and administrative data. We included all adults undergoing urgent air-medical transport in the Canadian province of Ontario between Jan. 1, 2004, and May 31, 2006. The primary outcome was in-transit critical events, which we defined as death, major resuscitative procedure, hemodynamic deterioration, or inadvertent extubation or respiratory arrest. RESULTS: We identified 19 228 patients who underwent air-medical transport during the study period. In-transit critical events were observed in 5.1% of all transports, for a rate of 1 event per 12.6 hours of transit time. Events consisted primarily of new hypotension or airway management procedures. Independent predictors of critical events included female sex (adjusted odds ratio [OR] 1.3, 95% confidence interval [CI] 1.1-1.5), assisted ventilation before transport (adjusted OR 3.0, 95% CI 2.3-3.7), hemodynamic instability before transport (adjusted OR 3.2, 95% CI 2.5-4.1), transport in a fixed-wing aircraft (adjusted OR 1.5, 95% CI 1.2-1.8), increased duration of transport (adjusted OR 1.02 per 10-minute increment, 95% CI 1.01-1.03), on-scene calls (adjusted OR 1.7, 95% CI 1.4-2.1) and type of crew (adjusted OR 0.6 for advanced care paramedics v. critical care paramedics, 95% CI 0.5-0.7). INTERPRETATION: Critical events occurred in about 1 in every 20 air-medical transports and were associated with multiple risk factors at the patient, transport and system levels. These findings have implications for the refinement of training of paramedic transport crews and processes for triage and transport.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".