Focused transesophageal echocardiography for emergency physicians—description and results from simulation training of a structured four-view examination
Bibliographic record
Abstract
BACKGROUND: Transesophageal echocardiography (TEE) offers several advantages over transthoracic echocardiography (TTE). Despite these advantages, use of TEE by emergency physicians (EPs) remains rare, as no focused TEE protocol for emergency department (ED) use has been defined nor have methods of training been described. OBJECTIVE: This study aims to develop a focused TEE examination tailored for the ED and to evaluate TEE skill acquisition and retention by TEE-naïve EPs following a focused 4-h curriculum. METHODS: Academic EPs were invited to participate in a 4-h didactic and simulation-based workshop. The seminar emphasized TEE principles and views obtained from four vantage points. Following the training, participants engaged in an assessment of their abilities to carry out a focused TEE on a high-fidelity simulator. A 6-week follow-up session assessed skill retention. RESULTS: Fourteen EPs participated in this study. Immediately following the seminar, 14 (100 %; k = 1.0) and 10 (71.4 %, k = 0.65) successfully obtained an acceptable mid-esophageal four-chamber and mid-esophageal long-axis view. Eleven (78.6 %, k = 1.0) participants were able to successfully obtain an acceptable transgastric short-axis view, and 11 (78.6 %, k = 1.0) EPs successfully obtained a bicaval view. Twelve participants engaged in a 6-week retention assessment, which revealed acceptable images and inter-rater agreement as follows: mid-esophageal four-chamber, 12 (100 %; k = 0.92); mid-esophageal long axis, 12 (100 %, k = 0.67); transgastric short-axis, 11 (91.7 %, k = 1.0); and bicaval view, 11 (91.7 %, k = 1.0). CONCLUSION: This study has illustrated that EPs can successfully perform this focused TEE protocol after a 4-h workshop with retention of these skills at 6 weeks.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".