Changes in Renal Function after Clinical Islet Transplantation: Four-Year Observational Study
Bibliographic record
Abstract
Tight glycemic control can reduce progression of diabetic nephropathy (DN) while the histological changes may regress after pancreas transplantation. Clinical islet transplantation (CIT) can restore euglycemia but the effects of CIT and concomitant immunosuppression on renal function are not known. Renal function (modification of diet in renal disease estimated glomerular filtration rate [GFR]) is reported in 41 type 1 diabetes subjects followed for 29.8 (6-57) months after CIT who received sirolimus and tacrolimus. HbA(1c) improved by 3 months (6.1 +/- 0.5 vs. 8.1 +/- 1.3%, p < 0.001) and was sustained. Over 4 years estimated GFR (eGFR) declined (repeated measures ANOVA: p = 0.0011). The median rate of change in eGFR was -0.39 mL/min/1.73 m(2)/month but was highly variable (range: +1.62 to -2.79 mL/min/1.73 m(2)/month). Progression of albuminuria was observed in ten individuals while regression of microalbuminuria was observed in only one (chi square = 22.51, df = 4, p = 0.0002). Despite improved glycemia, CIT and concomitant immunosuppression, was associated with a fall in eGFR and progression of albuminuria over 4 years of observation. The rate of decline in eGFR was extremely variable and difficult to predict. The risk of progressive nephrotoxicity with decline in eGFR should be discussed with prospective CIT candidates and the risk: benefit ratio carefully considered in individuals with pre-existing renal impairment.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".