Imaging of Vascular Complications and Their Consequences Following Transplantation in the Abdomen
Bibliographic record
Abstract
Transplantation is the surgical treatment of choice for end-stage organ failure. Transplantation procedures performed in the abdomen include liver, renal, pancreas, islet, intestinal, and multivisceral transplantations. Imaging plays a pivotal role in the posttransplantation setting for monitoring the transplant allograft and screening for complications. Knowledge of the surgical techniques employed in abdominal transplantation is essential because it facilitates radiologic understanding and interpretation of the posttransplantation anatomy. This article includes a basic description of the standard surgical techniques performed in the abdomen, with emphasis on the relevant vascular anastomotic reconstructions used. Posttransplantation complications can be broadly classified as vascular or nonvascular in origin. Many of these complications can be accurately depicted and characterized at imaging and dealt with definitively by using interventional radiology techniques, which can be graft- and life-saving and can obviate further complex surgical intervention. The article discusses imaging appearances of vascular complications and their consequences after transplantation in the abdomen. These vascular complications include arterial thrombosis, arterial stenosis, venous thrombosis and stenosis, arteriovenous fistula formation, and pseudoaneurysm formation. The relevant predisposing factors, clinical features, imaging appearances, and potential treatment options for vascular complications of various types of transplantation are presented in a logical and integrated fashion. Knowledge and imaging recognition of the posttransplantation vascular complications discussed in this article will aid radiologists in accurate imaging characterization and thereby facilitate appropriate clinical management and therapy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".