Self‐Learning Videos in Focused Transthoracic Echocardiography Training
Bibliographic record
Abstract
BACKGROUND: Focused transthoracic echocardiography (FOTE) is crucial for patients' bedside management. However, limited opportunities exist for practical FOTE training, prompting the use of simulation and self-learning videos to overcome this constraint. This study aimed to evaluate the impact of incorporating self-learning videos into a simulation FOTE training course. APPROACH: This was a prospective, randomized study involving University of Toronto internal medicine residents, who participated in a 2-h didactic and simulation FOTE training course before being randomized to a control group receiving written learning materials or an intervention group with additional self-directed learning videos. EVALUATION: Twenty-eight participants were randomized, and twenty-one (75%) completed the 1-month follow-up. Participants were assessed using a written test on image acquisition techniques and structure identification, scanning time and image quality on a simulator and self-reported scanning comfort, both pre-intervention and 1-month post-intervention. The groups had no significant difference in the time spent reviewing the material (1.5 vs. 1.4 h, p = 0.76). A significant increase in post-course scores was observed in all evaluations except for the control group's written test (p = 0.07). There were no significant between-group differences across the written test (p = 0.7), image quality (p = 0.6) and comfort level (p = 0.7). Compared to the control group, the intervention group exhibited a greater reduction in the scanning time (38 vs. 72 s, p = 0.02). IMPLICATIONS: FOTE training effectively increases theoretical knowledge and practical skills in a simulated setting. However, limited video utilization by participants precluded the inference of definitive conclusions on the impact of self-learning videos.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| 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".