Predictors of New Onset of Diabetes after Transplantation in Stable Renal Recipients
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Several groups identified pre-transplant factors which contribute to the development of new onset of diabetes after transplantation (NODAT). AIM: To identify post-transplant risk factors for NODAT. METHODS: 55 stable renal transplant patients were divided into group A of 34 recipients with normoglycemia and group B of 21 recipients with impaired fasting glucose. Markers including insulin, pro-insulin, soluble receptors for advanced glycated end products (sRAGE), adiponectin, malondialdehyde, homeostasis model assessment of insulin resistance (HOMA-IR), and beta-cell function were calculated at the outset and correlated, thereafter, with the later development of NODAT after a follow-up duration of 14.98 +/- 3.97 months. RESULTS: 11.8 and 19% of groups A and B respectively developed NODAT. Insulin, sRAGE, HOMA-IR and basal fasting plasma glucose correlated with the development of NODAT in univariate analysis. A baseline insulin level of 54.54 mU/l predicted the development of NODAT with a specificity of 95.45% and was the only significant factor in the multivariate analysis. beta-Cell function was not different among the three groups. CONCLUSIONS: A long prodrome of insulin resistance (IR) exists prior to development of NODAT. 50% of patients with NODAT will remit to a normoglycemic state. IR, rather than beta-cell dysfunction, precedes the development of NODAT. Serum insulin in stable non-diabetic renal transplant patients can be used as a confirmatory test to the development of future NODAT.
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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 | 0.000 | 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 | 0.000 |
| 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 it