Simulators and training models for diagnostic and therapeutic gastrointestinal endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Technical and Technology Review
Bibliographic record
Abstract
Gastrointestinal (GI) endoscopy comprises both diagnostic and therapeutic procedures involving the luminal GI tract as well as the biliary tree, liver, and pancreas. GI endoscopy is challenging to learn, requiring both cognitive (nontechnical) and technical skills, and requires extensive practice to attain proficiency. Simulation-based training has been shown to assist trainees and young endoscopists in acquiring new skills and accelerating the learning curve. Moreover, simulation-based training creates an ideal environment for trainees to initially learn and practice skills while making mistakes with no risk to patients.This review, divided in two parts, offers a comprehensive summary of the different classes of simulators available for GI endoscopic training.In Part I, only mechanical simulators are reported and described. In Part II, animal simulators (ex vivo/in vivo) and virtual reality models are detailed, together with prototypes that are currently not commercially available.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".