Adverse Events in Clinical Islet Transplantation: One Institutional Experience
Bibliographic record
Abstract
Islet transplantation is one of the most promising treatments for an unstable form of type 1 diabetes. However, islet transplantation still has some obstacles, such as low success rate of islet isolation, difficulty to obtain long-term insulin freedom, and adverse events related to transplant protocol. We describe the adverse events of current clinical islet transplantation at our institute in this report. Nine type 1 diabetic patients received 17 islet infusions from March 2005 to October 2008. The islet infusion procedure and immunosuppression regimen were based on a modified Edmonton protocol. Severe adverse events (SAEs) were defined as events that were more than grade 3 according to the Terminology Criteria for Adverse Events in Trials of Adult Pancreatic Islet Transplantation, version 4.1 (Collaborative Islet Transplant Registry, CITR). Sixteen events were reported as SAEs and among them 12 events were probably or definitely related to transplant protocols; all occurred within 1 year after infusion except for one. Five adverse events (31%) occurred within 10 days after transplantation and were related to infusion procedures. Seven events (44%) occurred after 50 days and were related to immunosuppressive therapy. SAEs related to the protocol included three events of elevated liver enzymes, two of hemorrhage into gall bladder or peritoneal cavity, two of neutropenia, two of infection, one of vomiting, one of diarrhea, and one of renal dysfunction. All events were grade 3, except for one case that was grade 4 of neutropenia. All SAEs resolved with no sequelae. Neoplasms and deaths were not observed in our study. The present study suggests need to improve both infusion procedure and immunosuppressive strategy from the view of preventing SAEs.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".