Human islet cell transplantation – future prospects
Bibliographic record
Abstract
BACKGROUND: Islet transplantation has the potential to cure diabetes mellitus. Nevertheless despite successful reversal of diabetes in many small animal models, the clinical situation has been far more challenging. The aim of this review is to discuss why insulin-independence after islet allotransplantation has been so difficult to achieve. METHODS: A literature review was undertaken using Medline from 1975 to July 2000. Results reported to the International Islet Transplant Registry (ITR) up to December 1998 were also analysed. RESULTS: Up to December 1998, 405 islet allotransplants have been reported the ITR. Of those accurately documented between 1990 and 1998 (n = 267) only 12% have achieved insulin-independence (greater than 7 days). However with refined peri-transplant protocols insulin independence at 1 year can reach 20%. CONCLUSIONS: There are many factors which can explain the failure of achieving insulin-independence after islet allotransplantation. These include the use of diabetogenic immunosuppressive agents to abrogate both islet allo-immunity and auto-immunity, the critical islet mass to achieve insulin-independence and the detrimental effects of transplanting islets in an ectopic site. However recent evidence most notably from the Edmonton group demonstrates that islet allotransplantation still has great potential to become an established treatment option for diabetic patients.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".