The role of thrombectomy and diffusion-weighted imaging with MRI in post-transplant renal vein thrombosis: a case report
Bibliographic record
Abstract
BACKGROUND: Surgical thrombectomy in the context of acute renal vein thrombosis (RVT) post-transplantation has had limited success, with considerable variation in the surgical techniques used. Unfortunately, it is usually followed by allograft nephrectomy within a few days if rapid allograft recovery does not ensue. We report a case of acute RVT in which nephrectomy was not performed despite a prolonged requirement for dialysis post-thrombectomy, but with recovery of renal function 2 weeks later. We also report the findings of serial MRI with diffusion-weighted imaging (DW-MRI) throughout the patient's recovery, which provided novel insights into allograft microvascular perfusion changes post-thrombectomy. CASE PRESENTATION: A 65-year old patient underwent living-unrelated kidney transplantation complicated by acute RVT. Surgical thrombectomy and irrigation led to a delayed, but significant, recovery of renal function. Serial non-contrast DW-MRI scanning was used to non-invasively assess microvascular renal blood flow post-operatively. Unlike standard Doppler ultrasonography, DW-MRI documented reduced microvascular perfusion initially, with gradual but incomplete recovery that mirrored the partial improvement in renal function. CONCLUSIONS: Our findings suggest that surgical thrombectomy may be more effective than previously described if followed by careful patient observation. Moreover, diffusion-weighted MRI appears to provide important insights into the pathophysiology of delayed graft function and deserves further investigation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| 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".