What Adult Electrocardiogram (ECG) Diagnoses or Findings are Most Important for Advanced Care Paramedics to Know?
Bibliographic record
Abstract
INTRODUCTION: The interpretation of electrocardiograms (ECGs) is an essential competency in modern paramedicine. Although educational guidelines for paramedic ECG interpretation exist, they are broad, not evidence-based, and lack prioritization in a prehospital clinical context. We conducted this study to gain consensus among stakeholders (EMS physicians, paramedic educators, and paramedic clinicians) regarding which ECG diagnoses or findings are most important for a practising advanced care paramedic to know. Methods: This study was an internet-based Delphi survey. We purposefully sampled participants in pairs (physician/paramedic) from all 10 Canadian provinces. Individuals rated a previously developed comprehensive list of emergency ECG diagnoses or findings on the importance of paramedic recognition and impact on prehospital care using a 4-point Likert scale. The consensus was achieved with a minimum of 75% agreement on Likert rating for a single diagnosis or finding during survey rounds one to three. When consensus was not reached, stability was defined as a shift of individual ratings between rounds of 20% or less. RESULTS: All 20 participants completed the first and second rounds of the survey, and 17 (85%) completed three rounds. Overall, 32 (26.4%) of 121 potentially important ECG diagnoses or findings reached consensus, 2 (1.7%) reached stability and 87 (71.9%) reached neither consensus nor stability. Twenty-one (17.4%) diagnoses or findings were considered "Very Important", six (4.9%) "Important", and five (4.1%) "Minimally Important". In the first round of the survey, the mean rating of the importance of a paramedic knowing a specific ECG diagnosis or finding was lower in the physician group than the paramedic group on 85 (72%) of 118 initial diagnoses or findings. CONCLUSION: We have created a list of ECG diagnoses or findings prioritized for the prehospital context that may assist paramedic educators in focusing on educational interventions. Many ECG diagnoses or findings failed to reach consensus or stability, demonstrating potential disagreement regarding clinical expectations for ECG knowledge among paramedics or physicians.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".