Iterative Design and Manufacturing of a 3D-Printed Pediatric Open and Laparoscopic Integrated Simulator for Hernia Repair (POLISHeR)
Bibliographic record
Abstract
BACKGROUND: Inguinal hernia is a common childhood pathology, making inguinal hernia repair (IHR) a key pediatric surgical procedure. Surgical success relies heavily on knowledge of groin anatomy, and both open and laparoscopic approaches require considerable repetition to master. As surgical simulators have been shown to improve performance for other surgical procedures, we developed a combined open and laparoscopic pediatric IHR simulator-named POLISHeR-to train residents, fellows, and practicing surgeons in both types of repair. METHODS: A CT scan of a 7-year-old was scaled down to create a virtual 3D model of a 2-year-old using our validated protocol for anatomical modelling. Physical replicas of the pelvis, abdominal wall, aorta, and inferior vena cava were 3D-printed to create a life-size unisex base for open and laparoscopic IHR, while a small mobile unisex base was 3D-printed for open IHR. We recruited six experienced surgeons and trainees to pilot the face validity of POLISHeR. RESULTS: After multiple iterations, we successfully developed a modular 3D-printed simulator for open and laparoscopic IHR. Printing the life-size base cost $331.69 USD, whereas the small base cost $17.54. An open modular cartridge cost $9.92 for females and $14.21 for males, whereas replacement parts cost under $1.30. A laparoscopic modular cartridge cost $6.16 for females and $10.91 for males, whereas replacement parts cost $0.28. Pilot study participants provided encouraging feedback with respect to POLISHER's face validity. CONCLUSIONS: Our low-cost simulator holds promise for enhancing training for pediatric IHR. Our next step is to conduct validation trials for trainees and practicing surgeons in both well-resourced and resource-limited settings. LEVEL OF EVIDENCE: Not applicable.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".