Simulation in endoscopy: Practical educational strategies to improve learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it