Human pancreatic islet transplantation: an update and description of the establishment of a pancreatic islet isolation laboratory
Bibliographic record
Abstract
Type 1 diabetes mellitus (T1DM) is associated with chronic complications that lead to high morbidity and mortality rates in young adults of productive age. Intensive insulin therapy has been able to reduce the likelihood of the development of chronic diabetes complications. However, this treatment is still associated with an increased incidence of hypoglycemia. In patients with “brittle T1DM”, who have severe hypoglycemia without adrenergic symptoms (hypoglycemia unawareness), islet transplantation may be a therapeutic option to restore both insulin secretion and hypoglycemic perception. The Edmonton group demonstrated that most patients who received islet infusions from more than one donor and were treated with steroid-free immunosuppressive drugs displayed a considerable decline in the initial insulin independence rates at eight years following the transplantation, but showed permanent C-peptide secretion, which facilitated glycemic control and protected patients against hypoglycemic episodes. Recently, data published by the Collaborative Islet Transplant Registry (CITR) has revealed that approximately 50% of the patients who undergo islet transplantation are insulin independent after a 3-year follow-up. Therefore, islet transplantation is able to successfully decrease plasma glucose and HbA1c levels, the occurrence of severe hypoglycemia, and improve patient quality of life. The goal of this paper was to review the human islet isolation and transplantation processes, and to describe the establishment of a human islet isolation laboratory at the Endocrine Division of the Hospital de Clínicas de Porto Alegre – Rio Grande do Sul, Brazil.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".