Visual Learning in Electrocardiography Training for Medical Residents: Comparative Intervention Study
Bibliographic record
Abstract
BACKGROUND: Although the training course of electrocardiogram (ECG) interpretation was started early in medical school, the accuracy in interpretation of 12-lead ECG is always a challenge issue. We conducted a pilot educational program to compare the effectiveness of a conventional didactic lecture, self-drawing (SD), and self-drawing following a flipped classroom approach (SDFC). OBJECTIVE: To evaluate the effectiveness of three instructional strategies-traditional didactic lecture, self-drawing (SD), and self-drawing following a flipped classroom approach (SDFC)-in improving electrocardiogram (ECG) interpretation skills among first-year postgraduate (PGY-I) medical residents. METHODS: This study was conducted by postgraduate-year (PGY)-I residents at MacKay Memorial Hospital over three years. The study enrolled 76 PGY-I residents, who were randomized into three groups: conventional control group (group 1), SD group (group 2) and SDFC group (group 3). All participants were provided with the same learning material and didactic lectures. Knowledge evaluation was performed using pre-tests and post-tests were conducted using questionnaires. RESULTS: The groups involving SD, whether combined with a flipped classroom or not, demonstrated better performance on the written summative examination. These findings highlight the benefits of SD in integrating theoretical knowledge with practical approaches to ECG interpretation. CONCLUSIONS: Our study demonstrated the promising effects of SD on the recognition of ECG presentations, which could make up for the inadequacies of traditional classroom teaching. It can be incorporated into routine teaching if proven successful in a larger cohort.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 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".