A Prospective Evaluation of the Utility of the Prehospital 12-lead Electrocardiogram to Change Patient Management in the Emergency Department
Bibliographic record
Abstract
OBJECTIVE: Retrospective research has shown that 19% of 12-lead prehospital electrocardiograms (prehospital ECGs) had clinically significant abnormalities that were not captured on the initial emergency department (ED) ECG and had the potential to change medical management. The purpose of this study was to prospectively determine how many prehospital ECGs had clinically significant abnormalities not present on the initial ED ECG and determine how many prehospital ECGs changed physician management. METHODS: We conducted a 3-month, prospective cohort study of patients who had a 12-lead prehospital ECG completed by EMS prior to arriving at one of two tertiary care EDs. STEMI bypass patients were excluded. Physicians reviewed the prehospital ECG to determine whether there were any clinically significant abnormalities present on the prehospital ECG not captured on the initial ED ECG. Physicians recorded if and how the prehospital ECG changed their management. RESULTS: A total of 281 patients were enrolled. Thirty-five (12.5%; 95% CI: 9.1%, 16.8%) prehospital ECGs showed changes that were not captured on the initial ED ECG (11 ST depression, 5 T-wave inversion [TWI], 2 ST depression and TWI, 12 arrhythmia, 2 arrhythmia with ST depression, 2 ST elevation, 1 unknown). Fifty-two (18.5%; 95% CI: 14.4%, 23.5%) prehospital ECGs influenced physician management. There were 30 (10.7%) instances where physicians were willing to refer the patient to an inpatient service based on information captured on the prehospital ECG, regardless if the initial ED ECG was normal. CONCLUSIONS: Prehospital ECGs show clinically significant abnormalities that are not always captured on the initial ED ECG. Prehospital ECGs have the potential to change the management of patients in the ED.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".