Allo- and auto-percutaneous intra-portal pancreatic islet transplantation (PIPIT) for diabetes cure and prevention: the role of imaging and interventional radiology
Bibliographic record
Abstract
Although the life expectancy of patients with type 1 diabetes mellitus (T1DM) has improved since the introduction of insulin therapy, the acute life-threatening and long-term complications from diabetes mellitus are significant causes of both mortality and morbidity. Percutaneous intra-portal pancreatic islet transplantation (PIPIT) is a minimally invasive, repeatable procedure which allows a β-cell replacement therapy through a liver islet engraftment, leading to insulin release and glycaemic control restoration in patients with diabetes. Allo-PIPIT, in which isolated and purified islets from cadaveric donor are used, does not require major surgery, and is potentially less expensive for the recipient. In case of long-term T1DM, islet-after-kidney (IAK) transplantation can simultaneously cure diabetes and chronic renal failure, while islet-transplant-alone (ITA) is performed in brittle, short-term T1DM, based on the infusion of an adequate islet mass and on a steroid-free immunosuppressive regimen according to the Edmonton protocol. Results of the Collaborative Islet Transplant Registry (CITR) demonstrate that allo-PIPIT reduces episodes of hypoglycemia and diabetic complications, and improves quality of life of diabetic patients. Auto-PIPIT, in which the own patient's islets are used, has been investigated as a preventive treatment for pancreatogenic diabetes in patients who undergo extensive pancreatectomy for malignant and non-malignant disease. This Review outlines the role of imaging and interventional radiology in allo- and auto-PIPIT.
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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) | 0.001 | 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".