Peri‐transplant glycaemic control as a predictor of pancreas transplant survival
Bibliographic record
Abstract
AIMS: The relationship between peri-transplant glycaemic control and outcomes following pancreas transplantation is unknown. We aimed to relate peri-transplant glycaemic control to pancreas graft survival and to develop a framework for defining early graft dysfunction. METHODS: Peri-transplant glycaemic control profiles over the first 5 days postoperatively were determined by an area under the curve [AUC; average daily glucose level (mmol/L) × time (days)] and the coefficient of variation of mean daily glucose levels. Peri-transplant hyperglycaemia was defined as an AUC ≥35 mmol/day/L (daily mean blood glucose ≥7 mmol/L). Risks of graft failure associated with glycaemic control and variability and peri-transplant hyperglycaemia were determined using covariate-adjusted Cox regression. RESULTS: We collected 7606 glucose readings over 5 days postoperatively from 123 pancreas transplant recipients. Glucose AUC was a significant predictor of graft failure during 3.6 years of follow-up (unadjusted HR [95% confidence interval] 1.17 [1.06-1.30], P = .002). Death censored non-technical graft failure occurred in eight (10%) recipients with peri-transplant normoglycaemia, and eight (25%) recipients with peri-transplant hyperglycaemia such that hyperglycaemia predicted a 3-fold higher risk of graft failure [HR (95% confidence interval): 3.0 (1.1-8.0); P = .028]. CONCLUSION: Peri-transplant hyperglycaemia is strongly associated with graft loss and could be a valuable tool guiding individualized graft monitoring and treatment. The 5-day peri-transplant glucose AUC provides a robust and responsive framework for comparing graft function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".