Proposed In‐Training Electrocardiogram Interpretation Competencies for Undergraduate and Postgraduate Trainees
Why this work is in the frame
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Bibliographic record
Abstract
Despite its importance in everyday clinical practice, the ability of physicians to interpret electrocardiograms (ECGs) is highly variable. ECG patterns are often misdiagnosed, and electrocardiographic emergencies are frequently missed, leading to adverse patient outcomes. Currently, many medical education programs lack an organized curriculum and competency assessment to ensure trainees master this essential skill. ECG patterns that were previously mentioned in literature were organized into groups from A to D based on their clinical importance and distributed among levels of training. Incremental versions of this organization were circulated among members of the International Society of Electrocardiology and the International Society of Holter and Noninvasive Electrocardiology until complete consensus was reached. We present reasonably attainable ECG interpretation competencies for undergraduate and postgraduate trainees. Previous literature suggests that methods of teaching ECG interpretation are less important and can be selected based on the available resources of each education program and student preference. The evidence clearly favors summative trainee evaluation methods, which would facilitate learning and ensure that appropriate competencies are acquired. Resources should be allocated to ensure that every trainee reaches their training milestones and should ensure that no electrocardiographic emergency (class A condition) is ever missed. We hope that these guidelines will inform medical education programs and encourage them to allocate sufficient resources and develop organized curricula. Assessments must be in place to ensure trainees acquire the level-appropriate ECG interpretation skills that are required for safe clinical practice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 it