Point: Steady Progress and Current Challenges in Clinical Islet Transplantation
Bibliographic record
Abstract
The field of β-cell replacement therapies has evolved substantially over the last decades. The lesson learned from recent islet transplantation trials in patients with unstable type 1 diabetes is that primary goals are the achievement of stable, normalized glycemic control in the absence of severe hypoglycemic episodes with improvement of quality of life and the prevention of progressive, chronic diabetes complications. Insulin independence, although desirable, should not be considered the main objective, particularly in light of the sustained positive effects achieved even with a “marginal” functional islet mass via restoration of C-peptide secretion and reduction of insulin requirements. As present limitations of islet transplantation are progressively overcome, the clinical application will greatly expand from the currently limited indication in controlled clinical research trials to more widely available cellular therapies and regenerative medicine solutions that will eventually be offered as standard treatment to the majority of patients with insulin-requiring diabetes. Vantyghem et al. (1) in the article in this issue of Diabetes Care evaluated the predictive value of primary graft function on long-term clinical outcomes of islet transplantation alone (ITA). Surrogate measures have been proposed to monitor or predict β-cell function, but they are not yet fully validated (2–4). In this report, the use of the β-score in the early posttransplant period allowed to quantify primary graft function that, when “optimal,” was associated with prolonged graft survival and better metabolic control following islet transplantation (1). In agreement with previous reports using the “Edmonton Protocol” (5–10), this trial resulted in a significant improvement of metabolic control and long-term graft function (∼70% having measurable C-peptide at 5 years). Importantly, the investigators also showed prolonged insulin independence in 57% of the patients at 5 years, with the subjects with optimal primary graft …
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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 | 0.061 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.012 | 0.025 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.018 | 0.023 |
| Insufficient payload (model declined to judge) | 0.025 | 0.017 |
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".