Simulator-Based Training in FoCUS with Skill-Based Metrics for Feedback: An Efficacy Study
Why this work is in the frame
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Bibliographic record
Abstract
Introduction: Focused Cardiac Ultrasound (FoCUS) is a relatively new technology that requires training and mentoring. The use of a FoCUS simulator is a novel training method that may prompt greater adoption of this technology by physicians at different levels of training and experience. The objective of this study was to determine if simulation training using an advanced echo simulator (Real Ultrasound®) is a feasible means of delivering training in FoCUS. Methods: Twenty-five residents and attending physicians participated in this study. After performing a pretest, training on the Real Ultrasound® was administered. Improvement was assessed immediately after simulator training. Additionally, some participants were retested six months after training to determine whether learned skills were retained. Results: Of the 25 participants recruited, all completed the pretest phase, and 17 completed the training and immediate posttest assessment. At pretest, the median angular deviation of acquired images from anatomically correct was 37°, which improved to 30° after training (p<0.002). Technical skill was largely maintained at six months of follow-up, with a median angle error of 27 and 31°, respectively (p=0.093) in 8 participants who completed the post and six-month retention assessments. The median pretest image interpretation score improved from 55% to 70% (p=0.028); median post and six month scores in the 8 participants were 72 and 68%, respectively (p=0.735). Conclusions: Simulation training in FoCUS significantly improves skills in image acquisition. These skills appear to be retained over time. This study adds support for the use of advanced echocardiographic simulators to enhance formal FoCUS training in a real-world setting.
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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) | 0.001 | 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 | 0.001 |
| 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