Follow-up Study of the First Successful Living Donor Islet Transplantation
Bibliographic record
Abstract
BACKGROUND: Islet transplantation has become an option for the treatment of insulin-dependent diabetes mellitus and is usually performed using brain-dead heartbeating donors. However, we have very limited number of such donors in Japan; therefore, it is not allowed to perform islet transplantation with brain-dead donors. In order to perform islet transplantation in Japan, we need to seek new donor resources. METHODS: We performed the first successful living-donor islet transplantation. In this case, the recipient had brittle diabetes with hypoglycemic unawareness. The donor was deemed qualified after undergoing both metabolic and preoperative assessments. Distal pancreatectomy was performed using open laparotomy and more than 400,000 islets were isolated and transplanted immediately. RESULTS: The recipient has been insulin independent posttransplant with positive C-peptide for more than one year. She no longer suffers from hypoglycemic unawareness and displayed a substantial improvement in hemoglobulin (Hb) A1C. The donor's clinical course was uneventful, which allowed her to return to her job within one month. She maintained normal fasting C-peptide and HbA1C levels during follow-up period. CONCLUSION: In our first case of living donor islet transplantation, both the donor and the recipient have been maintaining excellent glycemic control with no untreatable complications for more than one year.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".