Bibliographic record
Abstract
For 30 years there has been experimental work aimed at transplanting islets for the treatment of diabetes with a view to curing the disease and preventing the secondary complications. Many technical difficulties were experienced, first in isolating the islets without damaging them, and second in finding a suitable place to inject them, but until recently the results of a vascularized pancreas transplant have been superior to islet transplantation. In 2000, the group in Edmonton, headed by Shapiro, published encouraging results using a different immunosuppression in transplanting patients earlier in the course of their disease than had been attempted previously. The results were excellent at a year and good at 2 years in patients with Type I diabetes, however there was the rather worrying attrition at five years. Nevertheless, the Edmonton observations were proof of concept and have intensified interest in treating diabetes and other diseases where a specific protein synthesis was required by cell transplantation and/or genetic engineering. The recent interest in embryonic stem cells extenuated these efforts and progress is being made in defining the difficulties, which are greater than most workers would have predicted. In this review, the subject is discussed explaining where progress needs to be made in order to provide treatment that would be of value to patients.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.015 |
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".