LEARNING ERCP WITH THE BOŠKOSKI-COSTAMAGNA MECHANICAL SIMULATOR: A SINGLE TRAINEE'S EXPERIENCE
Bibliographic record
Abstract
Aims: Obtaining sufficient competence for ERCP practice requires long training and entails some well-known risks for patients. Numerous mechanical or electronic training modules have been developed recently to enhance ERCP learning curve with interesting results. In this study we have evaluated the impact of exposure to the Boškoski-Costamagna mechanical simulator on an ERCP trainee's learning curve. Methods: Simulator exposure was performed in three one-hour sessions on three consecutive days. Two sessions were performed with hands-on and verbal assistance from trainer and a last session with the trainee alone. The rate of deep biliary cannulation was measured for the trainee before and after his exposure to the simulator. Attention was paid to the cannulation time and complications rate. Results: A total of 30 consecutive patients were included, 15 before and 15 after simulator exposure. Biliary cannulation rate was significantly higher after exposure (20.0% before exposition vs. 66.7% after, p = 0.025 ). In addition, biliary cannulation time was significantly lower post exposure (20.0% of cannulation in the first 7 minutes before exposition vs. 66.7% after exposition p = 0.025 ). No difference was observed concerning immediate adverse events (13.3% before exposition vs. 0.0% after exposition p = 0.483 ). Conclusions: ERCP trainee's exposure to the Boškoski-Costamagna mechanical simulator at the beginning of training could enhance the rate and speed of deep biliary cannulation. Larger studies with multiple trainees and more variables are still needed to better evaluate the potential promising role of this simulator on ERCP trainees’ learning curve.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".