Insulin-Heparin Infusions Peritransplant Substantially Improve Single-Donor Clinical Islet Transplant Success
Bibliographic record
Abstract
BACKGROUND: Successful islet transplantation can result in insulin independence in many patients with type 1 diabetes mellitus, but it often requires more than one islet infusion. The ability to achieve insulin independence with a single donor is an important goal in clinical islet transplantation due to the limited organ supply. METHODS: We examined factors that may be associated with insulin independence after islet transplantation with islets from a single donor, using univariate and multivariate analysis. RESULTS: Thirteen of 85 (15.3%) achieved insulin independence after single-donor islet transplantation. Using multivariate analysis, only the use of insulin and heparin infusions peritransplant was a significant factor associated with insulin independence, with an adjusted odds ratio of 8.6 (95% confidence interval 2.0-37.0). Patients who had received insulin and heparin infusions peritransplant had greater indices of islet engraftment and a greater reduction in insulin use (80.1% + or - 4.3% vs. 54.2% + or - 2.8%, P<0.001) even if insulin independence was not achieved. CONCLUSIONS: Peritransplant intensive insulin and heparin enhances islet transplantation outcomes likely related in part to mitigation of the effects of the instant blood-mediated inflammatory reaction, combined with islet rest and avoidance of inflammation. It would be important to further investigate the effects of peritransplant insulin and heparin infusions on islet engraftment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".