Islet autotransplantation in focal intraductal papillary mucinous neoplasms: Evaluating feasibility, safety, and metabolic outcomes in pancreatic resection
Bibliographic record
Abstract
Islet auto transplantation (IAT) is a potential therapeutic option for patients undergoing pancreatectomy to preserve endocrine function, but its role in patients with intraductal papillary mucinous neoplasms (IPMNs) remains controversial due to oncological concerns. This study evaluated the feasibility, safety, and metabolic outcomes of IAT in 7 patients with focal IPMNs who underwent pancreatectomy between 2008 and 2023, following the Milan protocol. Primary outcomes included the technical success of islet isolation and the absence of tumor dissemination. Secondary outcomes included insulin independence and metabolic control posttransplant. Islet isolation success was variable, with 4 patients meeting the criteria for transplantation. The average islet yield was 1097 islet equivalents per kilogram of body weight (range: 219-1833 islet equivalents/kg). No patient experienced complications related to islet infusion, and there was no evidence of tumor recurrence or metastasis during a mean follow-up of 7.9 years (range: 3.99-11.88 years). IAT recipients demonstrated preserved insulin secretion, whereas nontransplanted patients developed diabetes. These findings support the feasibility and safety of IAT in carefully selected patients with focal IPMNs, providing promising metabolic outcomes. The results open the possibility to initiate larger cohort studies and explore the potential to expand the population of patients who could benefit from this approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 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".