Dialysis, Kidney Transplantation, or Pancreas Transplantation for Patients with Diabetes Mellitus and Renal Failure
Bibliographic record
Abstract
Patients with type 1 diabetes mellitus and end-stage renal disease may remain on dialysis or undergo cadaveric kidney transplantation, living kidney transplantation, sequential pancreas after living kidney transplantation, or simultaneous pancreas-kidney transplantation. It is unclear which of these options is most effective. The objective of this study was to determine the optimal treatment strategy for type 1 diabetic patients with renal failure using a decision analytic Markov model. Input data were obtained from the published medical literature, the United Network for Organ Sharing registry, and patient interviews. The outcome measures were life expectancy (in life-years [LY]) and quality-adjusted life expectancy (in quality-adjusted life-years [QALY]). Living kidney transplantation was associated with 18.30 LY and 10.29 QALY; pancreas after kidney transplantation, 17.21 LY and 10.00 QALY; simultaneous pancreas-kidney transplantation, 15.74 LY and 9.09 QALY; cadaveric kidney transplantation, 11.44 LY and 6.53 QALY; dialysis, 7.82 LY and 4.52 QALY. The results were sensitive to the value of several key variables. Simultaneous pancreas-kidney transplantation had the greatest life expectancy and quality-adjusted life expectancy when living kidney transplantation was excluded from the analysis. These data indicate that living kidney transplantation is associated with the greatest life expectancy and quality-adjusted life expectancy for type 1 diabetic patients with renal failure. Treatment strategies involving pancreas transplantation should be considered for patients with frequent metabolic complications of diabetes and for those patients who favor kidney-pancreas transplantation over kidney transplantation alone. For patients without a living donor, simultaneous pancreas-kidney transplantation is associated with the greatest life expectancy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".