Autologous Pancreatic Islet Cell Transplantation Following Pancreatectomy for Pancreas Diseases Other Than Chronic Pancreatitis: A 15-y Study of the Milan Protocol
Bibliographic record
Abstract
BACKGROUND: Pancreatogenic diabetes, a consequence of pancreatic tissue loss following pancreatectomy, poses a significant challenge for patients undergoing pancreatic surgery. Islet autotransplantation (IAT) offers a promising approach to prevent or alleviate pancreatogenic diabetes, but its application has been limited to individuals with painful chronic pancreatitis. METHODS: This study presents a 15-y clinical experience with the Milan Protocol, which expands IAT after pancreatectomy to a broader spectrum of patients with malignant and nonmalignant pancreatic diseases. The analysis evaluates feasibility, efficacy, and safety of IAT. Modified Igls criteria validated through the arginine test and mixed meal tolerance tests were used to assess long-term metabolic outcomes. RESULTS: Between November 2008 and June 2023, IAT procedures were performed on 114 of 147 candidates. IAT-related complications occurred in 19 of 114 patients (16.7%), with 5 being potentially serious. Patients exhibited sustained C-peptide secretion over the 10-y follow-up period, demonstrating a prevalence of optimal and good beta-cell function. Individuals who underwent partial pancreatectomy demonstrated superior metabolic outcomes, including sustained C-peptide secretion and a reduced risk of developing diabetes or insulin dependence compared with those who underwent total pancreatectomy. For patients who had total pancreatectomy, the quantity of infused islets and tissue volume were identified as critical factors influencing metabolic outcomes. An increased risk of recurrence or progression of baseline diseases was not observed in subjects with neoplasms. CONCLUSIONS: These findings provide valuable insights into the benefits and applications of IAT as a therapeutic option for pancreatogenic diabetes after pancreatic surgery, expanding its potential beyond painful chronic pancreatitis.
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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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".