Islet transplant – a secure future for cellular replacement therapy in diabetes
Bibliographic record
Abstract
Abstract Outcomes in clinical islet transplantation have recently been transformed, following the introduction of steroid‐free, sirolimus based immunosuppression and the development of more refined methods for islet isolation from the donor pancreas. With the ‘Edmonton Protocol’, currently 85% of patients (28 completed transplants) remain free of insulin at the one year time‐point, and three in four patients remain free beyond three years. Variants of this protocol have now been successfully replicated in over 150 patients worldwide. Complete normalisation of glycated haemoglobin suggests that this therapy is likely to prevent, stabilise and possibly reverse early complications of diabetes. Progress with single donor islet transplant will allow many more patients with type 1 diabetes to be treated, but will fail to meet the enormous demand for this type of therapy. Development of safer, low‐dose anti‐rejection therapies with very low risk of cancer or infectious complications is likely to further broaden the indications for islet transplant therapy in diabetes. If tolerance (or near‐tolerance) could be safely induced, protecting against both autoimmune and alloimmune rejection, then islet transplantation could possibly be applied in children, possibly allowing the use of laparoscopic live donor distal pancreatectomy to expand the available donor source. Recent progress both in stem cell biology and in xenotransplantation suggests that ultimately a limitless source of insulin secreting tissues will be found. The light at the end of the tunnel for patients with type 1 diabetes just got brighter. Copyright © 2003 John Wiley & Sons, Ltd.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".