Reduced Progression of Diabetic Retinopathy After Islet Cell Transplantation Compared With Intensive Medical Therapy
Bibliographic record
Abstract
BACKGROUND: Diabetic retinopathy is a major complication of type 1 diabetes and remains a leading cause of visual loss. There have been no comparisons of the effectiveness of intensive medical therapy and islet cell transplantation on preventing progression of diabetic retinopathy. METHODS: The British Columbia islet transplant program is conducting a prospective, crossover study comparing medical therapy and islet cell transplantation on the progression of diabetic retinopathy. Progression was defined as the need for laser treatment or a one step worsening along the international disease severity scale. An interim data analysis was performed after a mean 36-month follow-up postislet transplantation and these results are presented. RESULTS: The medical and postislet transplant groups were similar at baseline. Subjects after islet transplantation had better glucose control than the medically treated subjects (mean HbA1c 6.7%+/-0.9% vs. 7.5+/-1.2, P<0.01) and were C-peptide positive. Progression occurred significantly more often in all subjects in the medical group (10/82 eyes, 12.2%) than after islet transplantation (0/51 eyes, 0%) (P<0.01). Considering only subjects who have received transplants, progression occurred in 6/51 eyes while on medical treatment and 0/51 posttransplant (P<0.02). CONCLUSIONS: Progression of diabetic retinopathy was more likely to occur during medical therapy than after islet cell transplantation.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".