Long-term follow-up of patients with type 1 diabetes transplanted with neonatal pig islets
Bibliographic record
Abstract
Pancreas transplantation is an option to achieve better metabolic control and decrease chronic complications in patients with diabetes. Xenotransplantation becomes an important alternative. In this study, we show the clinical outcome of patients with type 1 diabetes transplanted with neonatal pig islets without immunosuppression. In a longitudinal study of 23 patients with type 1 diabetes, who received porcine islets between 2000 and 2004, we registered demographic and clinical characteristics every 3 months and chronic complications evaluation yearly. Porcine C-peptide was measured in urine samples under basal conditions and after stimulation with l-arginine. More than 50% were female, median current age was 20·8 years, median diabetes duration at transplantation 5·5 years, median current diabetes duration 11 years and median time post-transplantation 5·7 years. Their media of glycosylated haemoglobin reduced significantly after the first transplantation. Insulin doses remain with a reduction greater than 33% in more than 50% of the patients. Before transplantation, 14 of the 21 patients presented mild chronic complications and currently only two patients presented these complications. Porcine C-peptide was present in all urine samples under basal conditions and increased post-stimulation with l-arginine. These patients achieved an excellent metabolic control after the first transplantation. This could explain, as well as the remaining function of transplanted cells, the low frequency of chronic complications compared to patients with similar diabetes duration and age.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".