Life-threatening event in laparoscopic hepatic surgery: Training curriculum on sudden hepatic artery haemorrhage
Bibliographic record
Abstract
BACKGROUND: Exposure of the hepatic artery is a fundamental step in many surgeries, during which iatrogenic hepatic artery injury may occur. Although the incidence of hepatic artery haemorrhage is low, its occurrence can lead to life-threatening haemorrhage. It is difficult and dangerous to accumulate clinical experience in laparoscopic hepatic artery repair in actual patients, and simulation training models for laparoscopic hepatic artery repair are currently lacking. In this study, a 3D printed model was designed to simulate the training curriculum for sudden hepatic artery haemorrhage, but whether training with the 3D printed model could yield superior skill improvement for surgeons remained to be determined. METHODS: A new 3D printed model was designed for this study. Surgeons from the General Surgery Department of Sir Run Run Shaw Hospital participated in this simulation training. The surgical performance of each model was compared, and the authenticity of the model was evaluated and mechanically tested. RESULTS: Experienced surgeons performed better on the 3D printed model. After repeated training, inexperienced surgeons showed significant improvement of their laparoscopic hepatic artery repair skills. The authenticity of the model was generally satisfactory, but shortcomings persisted in the mechanical testing of artery wall tearing, necessitating further improvement. CONCLUSIONS: Few studies have investigated laparoscopic simulation training for sudden hepatic artery haemorrhage. This simulation model distinguishes surgeons with different levels of experience and allows those with less experience to improve their laparoscopic hepatic artery repair skills through training on the model.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 teacher head, 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".