A Novel Pelvic Hand-Sewn Bowel Anastomosis Simulator for Surgical Training <sup>*</sup>
Bibliographic record
Abstract
Hand-sewn bowel anastomosis (HSBA) is an important skill in general surgery. Due to technological advances such as surgical staplers for bowel, HSBA has become less common thus reducing general surgery residents' exposure to the technique. Difficult anatomical locations such as the pelvis face an even lower clinical exposure, though proficiency is crucial as consequences can include significant morbidity and mortality. Opportunities to develop this skill has been delegated primarily to surgical simulators. Given that cadaveric and porcine training is cost prohibitive and not readily available, the aim of this paper is to present a novel, reusable and cost-efficient simulator for HSBA in the pelvis. The simulator was built using advanced 3D printing methods and silicone-based materials to mimic a real pelvic cavity with emphasis on creating hyper realistic multi layered bowel. This simulator was reviewed by eight experts who performed an HSBA in the pelvic cavity followed by completing a Likert-based scale questionnaire resulting in an overall 92% score, endorsing it as a potential tool for general surgery training. In addition, the simulator is reproducible, provides tactical and haptic feedback to the user making it the first of its kind to combine HSBA in a difficult anatomical region such as the pelvis.Clinical Relevance-This novel pelvis HSBA simulator has the potential to make significant contributions to general surgery residency training to bridge the gap between textbooks and the operating room in a reproducible and cost-effective fashion.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".