Development of a novel ex vivo porcine laparoscopic Heller myotomy and Nissen fundoplication training model (Toronto lap-Nissen simulator)
Bibliographic record
Abstract
Background: Surgical trainees are required to develop competency in a variety of laparoscopic operations. Developing laparoscopic technical skills can be difficult as there has been a decrease in the number of procedures performed. This study aims to develop an inexpensive and anatomically relevant model for training in laparoscopic foregut procedures. Methods: An ex vivo, anatomic model of the human upper abdomen was developed using intact porcine esophagus, stomach, diaphragm and spleen. The Toronto lap-Nissen simulator was contained in a laparoscopic box-trainer and included an arch system to simulate the normal radial shape and tension of the diaphragm. We integrated the use of this training model as a part of our laparoscopic skills laboratory-training curriculum. Afterwards, we surveyed trainees to evaluate the observed benefit of the learning session. Results: Twenty-five trainees and five faculty members completed a survey regarding the use of this model. Among the trainees, only 4 (16%) had experience with laparoscopic Heller myotomy and Nissen fundoplication. They reported that practicing with the model was a valuable use of their limited time, repeating the exercise would be of additional benefit, and that the exercise improved their ability to perform or assist in an actual case in the operating room. Significant improvements were found in the following subjective measures comparing pre- vs. post-training: (I) knowledge level (5.6 vs. 8.0, P<0.001); (II) comfort level in assisting (6.3 vs. 7.6, P<0.001); and (III) comfort level in performing as the primary surgeon (4.9 vs. 7.1, P<0.001). The trainees and faculty members agreed that this model was of adequate fidelity and was a representative simulation of actual human anatomy. Conclusions: We developed an easily reproducible training model for laparoscopic procedures. This simulator reproduces human anatomy and increases the trainees’ comfort level in performing and assisting with myotomy and fundoplication.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".