Primary Graft Function, Metabolic Control, and Graft Survival After Islet Transplantation
Bibliographic record
Abstract
OBJECTIVE To investigate the influence of primary graft function (PGF) on graft survival and metabolic control after islet transplantation with the Edmonton protocol. RESEARCH DESIGN AND METHODS A total of 14 consecutive patients with brittle type 1 diabetes were enrolled in this phase 2 study and received median 12,479 islet equivalents per kilogram of body weight (interquartile range 11,072-15,755) in two or three sequential infusions within 67 days (44-95). PGF was estimated 1 month after the last infusion by the beta-score, a previously validated index (range 0-8) based on insulin or oral treatment requirements, plasma C-peptide, blood glucose, and A1C. Primary outcome was graft survival, defined as insulin independence with A1C < or =6.5%. RESULTS All patients gained insulin independence within 12 days (6-23) after the last infusion. PGF was optimal (beta-score > or =7) in nine patients and suboptimal (beta-score < or =6) in five. At last follow-up, 3.3 years (2.8-4.0) after islet transplantation, eight patients (57%) remained insulin independent with A1C < or =6.5%, including seven patients with optimal PGF (78%) and one with suboptimal PGF (20%) (P = 0.01, log-rank test). Graft survival was not significantly influenced by HLA mismatches or by preexisting islet autoantibodies. A1C, mean glucose, glucose variability (assessed with continuous glucose monitoring system), and glucose tolerance (using an oral glucose tolerance test) were markedly improved when compared with baseline values and were significantly lower in patients with optimal PGF than in those with suboptimal PGF. CONCLUSIONS Optimal PGF was associated with prolonged graft survival and better metabolic control after islet transplantation. This early outcome may represent a valuable end point in future clinical trials.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".