Islet transplantation in type I diabetes mellitus.
Bibliographic record
Abstract
For most patients with type I diabetes, insulin therapy and glucose monitoring are sufficient to maintain glycemic control. However, hypoglycemia is a potentially lethal side effect of insulin treatment in patients who are glycemically labile or have hypoglycemia-associated autonomic failure [1]. For those patients, an alternative therapy is beta cell replacement via pancreas or islet transplantation. Pancreas transplants using cadaveric donor organs reduce insulin dependence but carry risks involved in major surgery and chronic immunosuppression. Islet transplantation, in which islets are isolated from donor pancreases and intravenously infused, require no surgery and can utilize islets isolated from pancreases unsuitable for whole organ transplantation. However, islet transplantation also requires immunosuppression, and standard steroid regimens may be toxic to beta cells [2]. The 2000 Edmonton Trial demonstrated the first long-term successful islet transplantation by using a glucocorticoid-free immunosuppressive regimen (sirolimus and tacrolimus). The Clinical Islet Transplantation (CIT) Consortium seeks to improve upon the Edmonton Protocol by using anti-thymocyte globulin (ATG) and TNFα antagonist (etanercept). The trials currently in progress, in addition to research efforts to find new sources of islet cells, reflect enormous potential for islet transplantation in treatment of type I diabetes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".