The Impact of Pancreas Transplantation on Kidney Allograft Survival
Bibliographic record
Abstract
Whether pancreas after kidney transplantation (PAK) compromises kidney allograft survival, and what pre-PAK glomerular filtration rate (GFR) should be used to select patients for PAK is unclear. We analyzed all (n = 2776) PAK recipients in the United States between 1989 and 2007 and compared their risk of kidney failure to a comparator group of n = 13 635 young adult diabetic kidney only transplant recipients during the same time after accounting for selection bias by the use of a propensity score for PAK in a multivariate time to event analysis. In a secondary analysis, we determined the association of pre-PAK GFR with subsequent kidney allograft survival. Despite an increased risk of death early after pancreas transplantation, PAK recipients had a decreased long-term risk of kidney allograft failure compared to diabetic kidney only transplant recipients HR = 0.89; 95% CI: [0.78-1.00]; p = 0.05. An association of pre-PAK GFR with kidney survival was not evident until 3 years after pancreas transplantation, and patients with a pre-PAK GFR of 30-39 mL/min still attained 10-year post-PAK kidney survival of 69%. We conclude that PAK is associated with improved kidney allograft survival, and pre-PAK GFR 30-39 mL/min should not preclude PAK. Expanded use of PAK is warranted.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".