Simulator-Based Training in FoCUS with Skill-Based Metrics for Feedback: An Efficacy Study
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".