Depicting surgical anatomy of the porta hepatis in living donor liver transplantation
Bibliographic record
Abstract
Visualizing the complex anatomy of vascular and biliary structures of the liver on a case-by-case basis has been challenging. A living donor liver transplant (LDLT) right hepatectomy case, with focus on the porta hepatis, was used to demonstrate an innovative method to visualize anatomy with the purpose of refining preoperative planning and teaching of complex surgical procedures. The production of an animation-enhanced video consisted of many stages including the integration of pre-surgical planning; case-specific footage and 3D models of the liver and associated vasculature, reconstructed from contrast-enhanced CTs. Reconstructions of the biliary system were modeled from intraoperative cholangiograms. The distribution of the donor portal veins, hepatic arteries and bile ducts was defined from the porta hepatis intrahepatically to the point of surgical division. Each step of the surgery was enhanced with 3D animation to provide sequential and seamless visualization from pre-surgical planning to outcome. Use of visualization techniques such as transparency and overlays allows viewers not only to see the operative field, but also the origin and course of segmental branches and their spatial relationships. This novel educational approach enables integrating case-based operative footage with advanced editing techniques for visualizing not only the surgical procedure, but also complex anatomy such as vascular and biliary structures. The surgical team has found this approach to be beneficial for preoperative planning and clinical teaching, especially for complex cases. Each animation-enhanced video case is posted to the open-access Toronto Video Atlas of Surgery (TVASurg), an education resource with a global clinical and patient user base. The novel educational system described in this paper enables integrating operative footage with 3D animation and cinematic editing techniques for seamless sequential organization from pre-surgical planning to outcome.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".