Simulation in endoscopy: Practical educational strategies to improve learning
Bibliographic record
Abstract
In gastrointestinal endoscopy, simulation-based training can help endoscopists acquire new skills and accelerate the learning curve. Simulation creates an ideal environment for trainees, where they can practice specific skills, perform cases at their own pace, and make mistakes with no risk to patients. Educators also benefit from the use of simulators, as they can structure training according to learner needs and focus solely on the trainee. Not all simulation-based training, however, is effective. To maximize benefits from this instructional modality, educators must be conscious of learners' needs, the potential benefits of training, and associated costs. Simulation should be integrated into training in a manner that is grounded in educational theory and empirical data. In this review, we focus on four best practices in simulation-based education: deliberate practice with mastery learning, feedback and debriefing, contextual learning, and innovative educational strategies. For each topic, we provide definitions, supporting evidence, and practical tips for implementation.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".