15 Years Experience of Pancreas Transplant at the ALTRA Southern Alberta Transplant Program.
Bibliographic record
Abstract
Diabetes results in microvascular and macrovascular complications such as nephropathy, retinopathy and cardiovascular disease. Pancreas transplantation restores metabolic derangement and reduces or halts some of these complications therefore improves patient survival. Simultaneous pancreas and kidney transplant (SPK) is the most common approach while pancreas transplant after kidney transplant (PAK) is increasingly performed. We report our 15 years experience with 87 pancreas transplant procedures in 81 diabetic patients at the ALTRA Southern Alberta Transplant Program in Canada (73 SPK and 14 PAK, 3 patients received repeat pancreas transplant). The patient characteristics between these two groups are similar. Pre-emptive kidney transplantation is a major advantage of living kidney donation as all except one patient in PAK group had pre-transplant dialysis comparing to an average of 849 days of pre-transplant dialysis in SPK group. There is good patient survival in two groups after a median follow-up of 8.3 years (90.0±9.5% in PAK vs 83.2±5.3% in SPK). Excellent 10-year graft survival is achieved in both groups in kidney (85.7±13.2% in PAK vs 71.2±6.3% in SPK) and pancreas (61.2±14.0% in PAK vs 62.6±6.8% in SPK).Figure: No Caption available.Biopsy proven acute rejection rate was low and similar between SPK and PAK. In conclusion, both SPK and PAK transplantation lead to excellent long term patient and graft survival in type I diabetic patients. Pre-emptive kidney transplantation is a major advantage of living kidney donation followed by pancreas transplantation. DISCLOSURES:Tibbles, L.: Grant/Research Support, Pfizer.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".