The impact of internet and simulation‐based training on transoesophageal echocardiography learning in anaesthetic trainees: a prospective randomised study
Bibliographic record
Abstract
With the increasing role of transoesophageal echocardiography in clinical fields other than cardiac surgery, we decided to assess the efficacy of multi-modular echocardiography learning in echo-naïve anaesthetic trainees. Twenty-eight trainees undertook a pre-test to ascertain basic echocardiography knowledge, following which the study subjects were randomly assigned to two groups: learning via traditional methods such as review of guidelines and other literature (non-internet group); and learning via an internet-based echocardiography resource (internet group). After this, subjects in both groups underwent simulation-based echocardiography training. More tests were then conducted after a review of the respective educational resources and simulation sessions. Mean (SD) scores of subjects in the non-internet group were 28 (10)%, 44 (10)% and 63 (5)% in the pre-test, post-intervention test and post-simulation test, respectively, whereas those in the internet group scored 29 (8)%, 59 (10)%, (p = 0.001) and 72 (8)%, p = 0.005, respectively. The use of internet- and simulation-based learning methods led to a significant improvement in knowledge of transoesophageal echocardiography by anaesthetic trainees. The impact of simulation-based training was greater in the group who did not use the internet-based resource. We conclude that internet- and simulation-based learning methods both improve transoesophageal echocardiography knowledge in echo-naïve anaesthetic trainees.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.007 | 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".