Donor insulin therapy predicts early graft outcomes in pancreas and islet transplantation
Bibliographic record
Abstract
Introduction: Organ donors frequently develop hyperglycaemia, which is managed with insulin on intensive care. It is unclear what proportion of this hyperglycaemia is caused by reversible insulin resistance and how much is caused by beta-cell death. We hypothesised that Donor Insulin Use (DIU) on intensive care is a predictor of pancreas and islet transplant outcomes.<br/>Methods: Data on organ donors from the United Kingdom (UK) Transplant Registry was linked with regional data from our solid organ pancreas transplant (SPT) programme (2010-2015) and with national data from the UK Islet Transplant Consortium (2008-2016). Regression models determined associations between DIU and 3-month graft function and survival. <br/>Results: In 168 SPT recipients, DIR was associated with a higher rate of early graft loss from non-technical failure (failure rate: DIR vs. no-DIR: 6/71 [8.5%] vs 1/97 (1.0%], odds ratio, 95% CI: 8.9, 1.04-75.3, p=0.046) and lower 72-hours post-transplantation c-peptide (n=46; DIU vs. no-DIR: 1431 (1117) vs. 2496 (1702) pmol/L, p=0.005). In 91 islet cell transplant (ICT) recipients, DIR was associated with a higher 3-month HbA1c (n=74; DIU vs. no-DIU: 51 (15) vs. 45 (9) mmol/mol, p=0.044) and a higher 90-minute stimulated glucose levels (n=74, 15.9 (6.3) vs. 12.7 (4.3) mmol/l, p=0.012). <br/>Conclusion: In SPT and ICT recipients, DIU in intensive care is adversely related to measures of graft loss and function. Clinicians should be aware that DIU could be a marker of beta-cell death in donor pancreata. However, it would be premature to change clinical practice based on these preliminary data. Further research is required to; a) assess whether DIU can improve the prediction of graft outcomes; and b) develop objective tests of beta-cell death in potential donor pancreata. <br/>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".