The Use of Three‐Dimensional Printing Model in the Training of Choledochoscopy Techniques
Bibliographic record
Abstract
AIM: To evaluate the application value of a three-dimensional (3D) printing model in the training of choledochoscopy techniques. MATERIALS AND METHODS: Imaging data from two patients with biliary dilatation were used to produce two 3D reconstruction models which were subsequently constructed into 3D printing models (No. 1 and No. 2). Four hepatobiliary surgeons evaluated the anatomical accuracy and academic teaching value of the printed models. Twenty resident trainees with no prior experience in any kind of endoscopic techniques were randomly and symmetrically divided into two groups. The training group (A) used the 3D model No. 1 in the learning of biliary tract anatomy and practice techniques of choledochoscopy. The control group (B) got the virtual 3D image of the same model on computer for learning. After 4 weeks, the model No. 2 was used to reassess the trainees' subjective and objective progress in anatomy familiarity and choledochoscopy manipulations. RESULTS: All consulted surgeons agreed that the 3D models realistically reproduced the anatomy of the biliary system. All trainees in group A agreed or strongly agreed that the 3D models provided good anatomical realism, enhanced their experience in the training of choledochoscopy techniques, and aided in their learning of biliary anatomy. With the practice went on, they increased the accuracy and showed a reduction in operation time on the model No. 1. During final examination with model No. 2, the rate of correct anatomical structure identification in training group was significantly higher than group B (p < 0.05). CONCLUSION: The 3D printed biliary tract model is an excellent teaching tool in the training of choledochoscopy techniques. The 3D model is anatomically realistic and can improve the trainee's anatomical knowledge and endoscopic skills.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".