Logistics and Transplant Coordination Activity in the GRAGIL Swiss-French Multicenter Network of Islet Transplantation
Bibliographic record
Abstract
BACKGROUND: Since the Edmonton trial in 2000, increasing numbers of transplant centers have been implementing islet transplantation programs. Some institutions have elected to associate in multicenter networks, such as the Swiss-French GRAGIL (Groupe Rhin-Rhône-Alpes-Genève pour la Transplantation d'Ilots de Langerhans) consortium. METHODS: All pancreata offers to the University of Geneva Cell Isolation and Transplantation Center from within the network in 2002 and 2003 were reviewed. Islet preparations were attributed to the most suitable recipient on a centrally managed waiting list. All shipments were performed by ambulance in less than 5 hr. RESULTS: Over the period of study, 260 pancreata were offered, from a total of 1,304 cadaveric donors in the four allocation regions (20%). Fifty-two patients were on the waiting list at any time during this 2-year period. The percentage of organs offered varied in the range of 0.5% to 42%, depending on region of origin, with a correlation with number of patients on the waiting list in each region. Of these, 104 (40%) were accepted for processing. Ninety-two pancreata were actually processed, resulting in 42 islet preparations being transplanted. The number of international equivalents of transplanted preparations was 378,500+/-16,000 versus 165,400+/-15,400 (P<0.0001) for nontransplanted preparations. Total cold ischemia time was 6+/-0.3 hr for transplanted preparations versus 6.7+/-0.4 hr for nontransplanted preparations (not significant). CONCLUSIONS.: A high rate of pancreas offers, successful isolation, and islet transplantation can be achieved in multicenter networks such as GRAGIL. Such an approach can expand both the donor pool and the recipient population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".