Human Islet Transplantation: An Overview.
Bibliographic record
Abstract
Our aim is to compare human islet transplantation across North America (USA/Canada) and Europe. A literature search was conducted using Medline/Pubmed and Scielo with terms about islet transplantation from 2000 to 2013. We found 60 articles, but only 30 articles were selected. We look at sites of islet transplantation, number of islets, patient survival rate, islet survival rate, complications and immunossupression. We have found 268 patients (USA/Canada) and 104 patients (Europe). North America: site of transplantation - portal vein infusion - 262 (97.7%); mean number of islet transplant - 11,725 EI/Kg; 1 year and 3-5 years PSR and ISR - 100 (37.3%) and 16 (6%), respectively. The commonest complication was mouth ulcerations - 119 (44.4%); immunossupression - TAC+SIR+DAC - 227 (84.7%); corticosteroids - 44 (16.4%). Europe: site of transplantation - portal vein infusion - 99 (95.2% ); mean number of islet transplant - 10,898 EI/Kg; 1 year and 3-5 years PSR and ISR - 43 (41.34%) and 24 (23.07%), respectively. The commonest complication was anemia - 23 (8.6%); immunossupression - TAC + SIR + MYC - 87 (83.6%); corticosteroids - 40 (38.5%).In both continents 1 year PSR (37.3% / 41.34%) was similar, although 3-5 years PSR is higher in Europe than North America. Main transplantation technique was portal vein infusion in both continents. Mouth ulceration and anemia prevailed in NA and Europe as complications, respectively. The association of Tacrolimus, Sirolimus and Daclizumab (NA) / Mycophenolate (Europe) without corticosteroid was the standard immunossupression.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".