Interactive training with a novel simulation model for upper gastrointestinal endoscopic hemostasis improves trainee technique and confidence
Bibliographic record
Abstract
Abstract Background and study aims Endoscopic hemostasis is a life-saving procedure for gastrointestinal bleeding; however, training for it is often performed on real patients and during urgent situations that put patients at risk. Reports of simulation-based training models for endoscopic hemostasis are scarce. Herein, we developed a novel simulator called “Medical Rising STAR-Ulcer type” to practice endoscopic hemostasis with hemoclips and coagulation graspers. This study aimed to evaluate the reproducibility of the clinical difficulty of this model and the effectiveness of simulation-based training for clipping hemostasis. Patients and methods This was a prospective educational study. Fifty gastroenterology residents from Japan and Canada were recruited to participate in a simulation-based training program. The primary outcome was the success rate for clipping hemostasis. We measured differences in trainee subjective assessment scores and evaluated the co-occurrence network based on comments after training. Results The hemostasis success rate of the trainees significantly increased after instruction (64% vs. 86%, P < 0.05). The success rate for ulcers in the upper body of the stomach (59%), a high-difficulty site, was significantly lower than that for ulcers in the antrum, even after feedback and instruction. Trainee self-perceived proficiency and confidence significantly improved after simulation-based training (P < 0.05). Co-occurrence network analysis showed that trainees valued a structured learning approach, acknowledged simulator limitations, and recognized the need for continuous skill refinement. Conclusions Our study demonstrates the potential of our simulation-based training model as a valuable tool for improving technical skills and confidence in trainees learning to perform endoscopic hemostasis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".