Methods for the lmmunoisolation and Transplantation of Pancreatic Cells
Bibliographic record
Abstract
Although the administration of insulin by injection is clearly a life-saving intervention for patients devoid of β-cells, this approach falls short of the remarkable titration of insulin delivery and consequent control of glucose levels achieved by normal, healthy individuals. In the absence of the physiological control of the plasma glucose concentrations, the daily injection of insulin has not been able to prevent the common complications of the disease, namely nephropathy, retinopathy, and neuropathy, as well as vascular complications. This has been confirmed in the recent diabetes control and complications trial which has demonstrated that intensive treatment of patients with insulin-dependent diabetes mellitus (IDDM), with tight glycemic control close to the control range, effectively delays the onset, and slows the progression, of the various diabetic complications (1). Therefore, it becomes mandatory to develop methods, applicable early in the course of the disease, and in any type 1 diabetic patient, for obtaining perfect metabolic control without increasing the risk of severe hypoglycemia. Consequently, the transplantation of islet tissue, either as whole pancreas or as isolated islets, has been pursued, because these techniques can provide near-normal blood glucose control, and thus have the potential to prevent diabetic complications.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".