Islet-after-failed-pancreas and pancreas-after-failed islet transplantation: Two complementary rescue strategies to control diabetes
Bibliographic record
Abstract
For selected patients with type 1 diabetes, β-cell replacement is the treatment of choice, either by islet transplantation (ITX) or whole pancreas transplantation (PTX). When either modality fails, current practice is to consider retransplantation, or return to exogenous insulin. We investigate outcomes with PTX after failed ITX (PAI), and ITX after failed PTX (IAP). All patients receiving PAI or IAP at a single institution were identified. Donor and recipient variables were documented, including transplant outcomes analyzed for insulin requirement and metabolic control. Five subjects were listed for PAI, and 2 received transplants. Of the 4 listed for IAP, 3 have received transplants. The mean waitlist time was 4.5 ± 4.1 y for PAI and 0.35 ±0 .4 y for IAP (p = 0.08). Metabolic control was excellent after PAI, with 2/2 insulin-independent. After IAP, 1/2 achieved insulin independence and good metabolic control after 2 islet infusions. The third could not receive 2(nd) infusion and presented c-peptide levels < 0.1 nmol/L. Both strategies are feasible. The outcomes after PAI in our center must be offset by much longer waitlist time due to the sensitization status of these patients. Data from multicentre experience will allow more robust comparative outcomes to be made, the current observations being restricted to a limited patient set.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".