Bibliographic record
Abstract
This article, derived from a meeting on advances in transplantation but incorporating new data, focuses on the aspect that was discussed in detail by Professor James Shapiro, of the University of Alberta, Canada. Diabetes mellitus (DM) is increasing worldwide1 and now affects up to 5% of the population in the UK. Roughly 10% of these patients have type I DM, caused by insulin deficiency secondary to autoimmune destruction of pancreatic islet cells. DM is associated with life-threatening metabolic or vascular complications in 30% of patients2. According to data from the UK Renal Registry, 20% of all new patients in the UK under the age of 65 years requiring dialysis treatment have end-stage renal failure secondary to DM. In addition to renal care, patients with diabetes require a diverse range of services including cardiology and cardiac surgery, vascular surgery and ophthalmology. The Diabetes Control and Complications Trial (DCCT)3 showed that tight glycaemic control delays and reduces diabetic complications. However, intensive insulin treatment is poorly tolerated by many patients and will decrease the number of patients who develop microvascular complications by no more than 30-40%. Further, a small but substantial number of patients have life-threatening hypoglycaemic episodes despite scrupulous attention to their insulin regimens. Therefore, to improve diabetic care, the need is for treatments that achieve metabolic stability and prevent microvascular complications. Reports from Professor Shapiro's team describe major improvements in the early clinical outcome of patients with type I DM treated with human islet cell transplantation by newly developed protocols. Here we discuss the key areas they report that contribute to improvements in the outcome of islet cell transplantation, particularly the use of novel immuno-suppressive strategies.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.024 |
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".