Optimizing simulator‐based training for emergency transesophageal echocardiography: A randomized controlled trial
Bibliographic record
Abstract
Abstract Background Resuscitative clinician‐performed transesophageal echocardiography (TEE) is a relatively novel ultrasound application; however, optimal teaching methods have not been determined. Previous studies have demonstrated that variable practice (VP), where practice conditions are changed, may improve learning of procedural skills compared with blocked practice (BP), where practice conditions are kept constant. We compared VP and BP for teaching resuscitative TEE to emergency medicine residents using a simulator. Methods Emergency medicine residents with no prior TEE experience were randomized to the BP or VP groups. The BP group practiced 10 repetitions of a fixed five‐view TEE sequence, while the VP group practiced 10 different random five‐view TEE sequences on a simulator. Participants completed a performance assessment immediately after training and a transfer test 2 weeks after training. Ultrasound images and transducer motion metrics were captured by the simulator for blinded analysis. The primary outcome was the percentage of successful views on the transfer test. Results Twenty‐eight participants completed the study (14 in the BP group, 14 in the VP group). The BP group had a higher rate of successful views compared with the VP group on the transfer test (93.6% vs. 77.6%; p = 0.002). The BP group also had higher image quality on a 5‐point scale (3.3 vs. 2.9; p = 0.01) and fewer probe angular changes (2982.5 degrees vs. 4239.8 degrees; p = 0.04). There were no statistically significant differences between the groups for the rate of correct diagnoses, confidence level, or scan time. Conclusions Practicing a fixed sequence of views was more effective than a variable sequence of views for learning resuscitative TEE on a simulator. These results should be validated in TEE scans performed in the clinical environment.
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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 | 0.002 | 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 | 0.000 |
| 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".