Epidemiology of Adverse Events in Air Medical Transport
Bibliographic record
Abstract
OBJECTIVES: This observational study determined frequency and describes all-cause adverse event epidemiology in a large air medical transport system. METHODS: Records of a mandatory reporting system were reviewed and a data set containing all of the patient care records was searched to identify aviation- and non-aviation-related adverse events. Two reviewers independently identified adverse events and categorized them using an established taxonomy. Descriptive statistics were used to report adverse events, with frequency calculated per 1,000 flights and 1,000 hours flown. RESULTS: Between January 1, 2002, and June 30, 2005, there were 1,447 reports, of which 598 included an adverse event. Case-finding identified an additional 125. A complete report was available in 680 of 723 (94.1%) events. There were 58,956 flights and 103,632 hours flown during the study period, for a rate of 11.53 adverse events per 1,000 flights (95% CI = 10.7 to 12.4 adverse events) or 6.56 per 1,000 hours flown (95% CI = 6.1 to 7.1 adverse events). The frequencies of events by category were as follows: communication (229; 33.7%), transport vehicle (143; 21.0%), medical equipment (88; 12.9%), patient management (77; 11.4%), clinical performance (68; 10.0%), weather (30; 4.4%), unclassified (24; 3.5%), and patient factors causing death (21; 3.1%). There was possible patient harm in 117 events. CONCLUSIONS: Air medical transport is associated with a low incidence of adverse events and possible patient harm. Communication problems were the most common cause of an event. Determining event epidemiology is necessary to identify modifiable factors, propose solutions to decrease the adverse events, and direct future efforts to improve safety.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".