301.2: A decision-making approach based on computer modelling of blood flow in hepatic vessels.
Bibliographic record
Abstract
Introduction: Tactical issues of vascular reconstruction during liver transplantation remain largely unresolved. Particular aspects are related liver transplantation, transplantation after the TIPSS procedure, in the presence of pre-existing portal vein thrombosis. The use of mathematical modeling of blood flow in silico seems promising for solving a number of similar issues. Materials and methods: We analyzed 700 cases of treatment of transplanted patients at the Moscow Liver Transplantation Center, 200 of whom had thrombosis of the portal vein system. Before transplantation, all patients underwent CT scanning of the abdominal organs with four-phase contrast, Doppler ultrasound blood flow, and intraoperative direct ultrasound flowmetry. DICOM data segmentation was performed using Dragonfly software (Object Research Systems, Canada) on the basis of the laboratory of mathematical modeling in medicine of the Moscow Medical University “Reaviz”. Methods of computational hemodynamics were implemented using the Visual-CFD application for OpenFOAM environment (ESI, France) and FlowVision app (TeSis, Moscow).Results: When assessing the sensitivity/specificity of the modeling, we obtained the results of a 3% discrepancy between the calculated data and the postoperative flowmetry data. The sensitivity of the applied prognostic model is 97%, specificity 99%. No significant differences between nosological forms and situations were recorded during modeling. When using preoperative prediction of blood flow using the proposed model, it was found that the use of a jump graft provides the most favorable restoration of blood flow compared to cava-portal implantation in the presence of a predicted blood flow in the portal vein exceeding 950+-40 ml/min. The use of caval reconstruction techniques, which prevent deterioration of blood flow in the liver veins, also has a significant effect on portal blood flow, which made it possible to increase the flow to the liver in 17 cases of portal reconstruction. However, the volume of observations in this matter is still small and requires further assessment Conclusion: Decision-making in vascular reconstruction during liver transplantation can be objectified using modern computing technologies, which creates prospects for further development of this area.
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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 | 0.000 | 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".