Clinical islet transplantation: is the future finally now?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Clinical pancreatic islet transplantation has evolved into a routine means to restore glycemic control in patients with type 1 diabetes mellitus (T1DM) suffering from life-threatening hypoglycemia and severe glucose liability. This chapter examines the current progress in islet transplantation while outlining the remaining limitations preventing this life-altering therapy's application to the broader T1DM population. RECENT FINDINGS: Islet transplantation has recently been demonstrated to provide superior glycemic control with reduced glucose lability and hypoglycemic events compared with standard insulin therapy. Transplant outcomes have steadily improved, in part, reflective of refinements, including more optimal islet donors and isolations, safer transplant techniques and more effective anti-inflammatory and immunomodulatory intervention. Furthermore, latest insulin independence rates 5-years posttransplant have reached parity with pancreas transplantation. Successful completion of a recent National Institutes of Health-sponsored Phase III multicenter clinical allogeneic islet transplantation trial confirmed the safety and efficacy of this therapeutic modality and will be used in the Biological Licensure Application by the United States Food and Drug Administration. SUMMARY: Implementation of novel immunosuppression, antiinflammatories, first-in-human stem cell and extrahepatic transplant site trials into clinical investigation has positioned β-cell replacement to become the mainstay treatment for all T1DM patients in the near future.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".