Long-term benefits of pancreas transplantation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Pancreas transplantation has emerged as an effective treatment for patients with diabetes mellitus, especially those with established end-stage renal disease. Surgical and immunosuppressive advances have significantly improved allograft survival. With more recipients enjoying normoglycemia for longer periods of time, the opportunity to study more closely the effects of pancreas transplantation has arisen. This review will focus on these long-term benefits. RECENT FINDINGS: The field of pancreas transplantation has been limited by a lack of randomized, controlled trials and relatively poor graft survival rates historically, however we can still glean many important points from the existing literature. The procedure reduces mortality compared with diabetic kidney transplant recipients and waitlisted patients. Improvements in diabetic nephropathy and retinopathy have also been demonstrated. Pancreas transplantation can improve cardiovascular risk profiles, improve cardiac function and decrease cardiovascular events. Lastly, improvements in diabetic neuropathy and quality of life can result from pancreas transplantation. SUMMARY: Pancreas transplantation remains the most effective method to establish durable normoglycemia for patients with diabetes mellitus. Well designed clinical studies to assess outcomes and adverse events will be of paramount importance in providing optimal care to patients with diabetes mellitus.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".