Pre-transplantation glucose testing for predicting new-onset diabetes mellitus after renal transplantation.
Bibliographic record
Abstract
AIM: New-onset diabetes after renal transplantation (NODAT) adversely affects graft and patient survival. However, NODAT risk based on pre-transplant blood glucose (BG) levels has not been defined. Our goal was to identify the best pre-transplant testing method and cut-off values. MATERIALS AND METHODS: We performed a case-control analysis of non-diabetic recipients who received a live donor allograft with at least 6 months post-transplant survival. Pre-transplant glucose abnormalities were excluded through 75 g oral glucose tolerance testing (OGTT) and random BG (RBG) measurement. NODAT was defined based on 2003 Canadian Diabetes Association criteria. Multivariate logistic and Cox regression analysis was performed to determine independent predictor variables for NODAT. Receiver-operating-characteristic (ROC) curves were constructed to determine threshold BG values for diabetes risk. RESULTS: 151 recipients met initial entry criteria. 12 had pre-transplant impaired fasting glucose and/or impaired glucose tolerance, among who 7 (58%) developed NODAT. In the remaining 139, 24 (17%) developed NODAT. NODAT risk exceeded 25% for those with pre-transplant RBG > 6.0 mmol/l and 50% if > 7.2 mmol/l. Pre-transplant RBG provided the highest AUC (0.69, p = 0.002) by ROC analysis. Increasing age (p = 0.025), acute rejection (p = 0.011), and RBG > 6.0 mmol/l (p = 0.001) were independent predictors of NODAT. CONCLUSION: Pre-transplant glucose testing is a specific marker for NODAT. Patients can be counseled of their incremental risk even within the normal BG range if the OGTT is normal.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".