A Multi-Year Analysis of Islet Transplantation Compared With Intensive Medical Therapy on Progression of Complications in Type 1 Diabetes
Bibliographic record
Abstract
BACKGROUND: We hypothesized that transplantation of islets into type 1 diabetics could improve outcomes of glucose metabolism, renal function, retinopathy, and neuropathy compared with intensive medical therapy. METHODS: We conducted a prospective, crossover, cohort study of intensive medical therapy (group 1) versus islet cell transplantation (group 2) in 42 patients. All were enrolled in group 1 then 31 crossed over with group 2 when islet donation became available. Transplantation was performed by portal venous embolization of more than 12,000 islet equivalents/kg body weight under cover of immunosuppression with antithymocyte globulin, tacrolimus, and mycophenolate. Outcome measures were HbA1c, change in glomerular filtration rate (GFR), progression of retinopathy, and change in nerve conduction velocity. This report details interim analysis of outcomes after 34+/-18 months (group 1) and 38+/-18 months (group 2). RESULTS: HbA1c (%) in group 1 was 7.5+/-0.9 versus 6.6+/-0.7 in group 2 (P<0.01). GFR (mL/min/month) declined in both groups (group 1 -0.45+/-0.7 vs. group 2 -0.12+/-0.7, P=0.1). Slope of the GFR decline in group 1 was significantly more than 0. Retinopathy progressed in 10 of 82 eyes in group 1 versus 0 of 51 in group 2 (P<0.01). Nerve conduction velocity (m/sec) remained stable in group 1 (47.8+/-5 to 47.1+/-5 m/sec) and group 2 (47.2+/-4.5 to 47.7+/-3.5). CONCLUSION: Islet transplantation yields improved HbA1c and less progression of retinopathy compared with intensive medical therapy during 3 years follow-up.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".