Simulation training of laparoscopic biliary-enteric anastomosis with a three-dimensional-printed model leads to better skill transfer: a randomized controlled trial
Bibliographic record
Abstract
AIM: A new simulation model and training curriculum for laparoscopic bilioenteric anastomosis has been developed. Currently, this concept lacks evidence for the transfer of skills from simulation to clinical settings. This study was conducted to determine whether training with a three-dimensional (3D) bilioenteric anastomosis model result in greater transfer of skills than traditional training methods involving video observation and a general suture model. METHODS: Fifteen general surgeons with no prior experience in laparoscopic biliary-enteric anastomosis were included in this study and randomised into three training groups: video observation only, practice using a general suture model, and practice using a 3D-printed biliary-enteric anastomosis model. Following five training sessions, each surgeon was asked to perform a laparoscopic biliary-enteric anastomosis procedure on an isolated swine organ model. The operative time and performance scores of the procedure were recorded and compared among the three training groups. RESULTS: The operation time in the 3D-printed model group was significantly shorter than the suture and video observation groups ( P =0.040). Furthermore, the performance score of the 3D-printed model group was significantly higher than those of the suture and video observation groups ( P =0.001). Finally, the goal score for laparoscopic biliary-enteric anastomosis in the isolated swine organ model was significantly higher in the 3D model group than in the suture and video observation groups ( P =0.004). CONCLUSIONS: The utilisation of a novel 3D-printed model for simulation training in laparoscopic biliary-enteric anastomosis facilitates improved skill acquisition and transferability to an animal setting compared with traditional training techniques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".