Design and validation of a low-cost modular simulator for training in neonatal laparotomy and jejunoileal atresia repair
Bibliographic record
Abstract
PURPOSE: Surgical treatment of neonatal jejunoileal atresia requires highly specialized skills, but its rarity makes training opportunities sparse. We aimed to address this by developing and evaluating a low-cost, high-fidelity, modular simulator for comprehensive training in neonatal laparotomy and small bowel atresia repair. METHODS: Our design consisted of three primary components: bowel, abdominal wall, and abdominal cavity. 3D-printed molds were used to cast silicone models of type II and type IIIa atretic bowel with its mesentery. The abdominal wall was created by layering molded silicone and fabric. A small 3D-printed enclosure with an opening and bony landmarks was designed to simulate the neonatal abdomen. The material cost per model was ∼$16.88 USD, with each subsequent use costing ∼$5.09 USD. Eight expert surgeons and eight surgical trainees performed simulated atresia repairs and stoma creations. Participants rated face and content validity on a 5-point Likert scale. Videos of the simulated procedures were evaluated by an attending pediatric surgeon to assess construct validity by comparing performance between experts and trainees. RESULTS: The highest mean realism ratings were for surface anatomy (4.06) and anatomical structure (4.06). Content validity responses were positive (mean ratings >4), indicating that participants found the model thorough and valuable. Trainees made significantly more mistakes (p = 0.001) and completed fewer tasks (p = 0.01) than experts. CONCLUSION: Our simulator demonstrated strong face, content, and construct validity. It offers an accessible, cost-effective training tool, with promise for implementation in both high- and low-resource settings with limited surgical training opportunities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".