Simulation‐based teaching versus point‐of‐care teaching for identification of basic transoesophageal echocardiography views: a prospective randomised study
Bibliographic record
Abstract
In recent years, the use of transoesophageal echocardiography has increased in anaesthesia and intensive care. We explored the impact of two different teaching methods on the ability of echocardiography-naïve subjects to identify cardiac anatomy associated with the 20 standard transoesophageal echocardiography imaging planes, and assessed trainees' satisfaction with these methods of training. Fifty-two subjects were randomly assigned to one of two groups: a simulation-based and a theatre-based teaching group. Subjects undertook video-based tests comprised of 20 multiple choice questions on echocardiography views before and after receiving echocardiography teaching. Subjects in simulation- and theatre-based teaching groups scored 40% (30-40 [20-50])% and 35% (30-40 [15-55])% in the pre-test, respectively (p = 0.52). Following echocardiography teaching, subjects within both groups improved upon their pre-test knowledge (p < 0.001). Subjects in the simulation-based teaching group significantly outperformed their theatre-based group counterparts in the post-intervention test (p = 0.0002).
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".