Defining Outcomes for β-cell Replacement Therapy in the Treatment of Diabetes
Bibliographic record
Abstract
β-cell replacement therapy, available currently as pancreas or islet transplantation, has developed without a clear definition of graft functional and clinical outcomes. The International Pancreas and Islet Transplant Association and European Pancreas and Islet Transplantation Association held a workshop to develop consensus for an International Pancreas and Islet Transplant Association and European Pancreas and Islet Transplant Association Statement on the definition of function and failure of current and future forms of β-cell replacement therapy. There was consensus that β-cell replacement therapy could be considered as a treatment for β-cell failure, regardless of etiology and without requiring undetectable C-peptide, accompanied by glycemic instability with either problematic hypoglycemia or hyperglycemia. Glycemic control should be assessed at a minimum by glycated hemoglobin (HbA1c) and the occurrence of severe hypoglycemia. Optimal β-cell graft function is defined by near-normal glycemic control (HbA1c ≤6.5% [48 mmol/mol]) without severe hypoglycemia or requirement for insulin or other antihyperglycemic therapy, and with an increase over pretransplant measurement of C-peptide. Good β-cell graft function requires HbA1c less than 7.0% (53 mmol/mol) without severe hypoglycemia and with a significant (>50%) reduction in insulin requirements and restoration of clinically significant C-peptide production. Marginal β-cell graft function is defined by failure to achieve HbA1c less than 7.0% (53 mmol/mol), the occurrence of any severe hypoglycemia, or less than 50% reduction in insulin requirements when there is restoration of clinically significant C-peptide production documented by improvement in hypoglycemia awareness/severity, or glycemic variability/lability. A failed β-cell graft is defined by the absence of any evidence for clinically significant C-peptide production. Optimal and good function are considered successful clinical outcomes.
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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 | 0.044 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".