Safe Transport of Patients with Acute Coronary Syndrome or Cardiogenic Shock by Skilled Air Medical Crews
Bibliographic record
Abstract
INTRODUCTION: Acute coronary syndrome (ACS) is a spectrum of disease that includes unstable angina (UA), non?ST-segment elevation myocardial infarction (NSTEMI), and ST-segment elevation myocardial infarction (STEMI). Cardiogenic shock is a severe complication of an ACS. Evidence suggests that emergent primary coronary intervention is the treatment of choice for patients with acute STEMI, and patients who have hemodynamic instability or suffer a major complication of therapy also require emergent intervention. These patients may require emergent interfacility transfer for this intervention. OBJECTIVE: This study examined ACS patient transfers to determine the incidence of adverse events (AEs) during transfer in a large transport medicine service. METHODS: This was a retrospective review of prospectively collected data for air medical transfer of ACS or cardiogenic shock patients in Ontario, Canada, from January 2005 to June 2007. Call records and patient care reports were screened for AE identifiers, including resuscitation medication and procedure and unstable cardiac rhythms. Each chart with an AE was independently reviewed by two investigators, with consensus in cases of disagreement, to determine the incidence and type of AE. RESULTS: During the study period, there were 2,258 transfers for which the patient had a primary diagnosis of ACS or cardiogenic shock. The mean age was 62 years (range 24-91 years), and 68% of the patients were male. Investigators identified one or more AEs that occurred during 127 (5.6%) patient transfers, with hypotension (n = 80), increasing chest pain (n = 52), and arrhythmia (n = 18) as the three most common AEs. There was one death in flight. Management of the AEs was within the scope of practice of transport personnel in all but one case. CONCLUSION: The incidence of AEs in air medical transport of ACS patients is low. Air medical crews can safely transport this potentially unstable patient population.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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, 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".