Transforming orthotopic liver transplantation: Innovative dry-lab simulation model in mice
Bibliographic record
Abstract
He use of mouse liver transplantation models remains crucial for answering questions regarding organ transplantation, tissue-resident immunity and tumor biology. The learning curve for this complex procedure involves use of animals without creating scientific output. We present a recipient mouse and donor liver model with realistic vessels for training in mouse liver transplantation, designed to reduce the number of animals needed during the training. A 3D-model of the liver, skeleton and vessels (vena cava, hepatic artery and portal vein) was created using computed tomography images of a real mouse and 3D-printing to simulate orthotopic liver transplantation. Microsurgical experts from multiple research groups evaluated this model for usability, procedural details, and on how well the materials mimicked the real tissue. We successfully created an artificial model featuring the mouse body, organs, and vessels needed for initial training in mouse liver transplantation. The evaluation found it to realistically mimic the confined space of the surgical site and determined that it could be used for vessel anastomoses with suturing and the cuff-technique. This simulation model enables a cost-effective approach for basic training in mouse liver transplantation that can easily be reproduced to reduce animal use in the training process.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".