Incidence of hyperglycemia and diabetes and association with electrolyte abnormalities in pediatric solid organ transplant recipients
Bibliographic record
Abstract
BACKGROUND: Posttransplant hyperglycemia is an important predictor of new-onset diabetes after transplantation, and both are associated with significant morbidity and mortality. Precise estimates of posttransplant hyperglycemia and diabetes in children are unknown. Low magnesium and potassium levels may also lead to diabetes after transplantation, with limited evidence in children. METHODS: We conducted a cohort study of 451 pediatric solid organ transplant recipients to determine the incidence of hyperglycemia and diabetes, and the association of cations with both endpoints. Hyperglycemia was defined as random blood glucose levels ≥11.1 mmol/L on two occasions after 14 days of transplant not requiring further treatment. Diabetes was defined using the American Diabetes Association Criteria. For magnesium and potassium, time-fixed, time-varying and rolling average Cox proportional hazards models were fitted to evaluate the association with hyperglycemia and diabetes. RESULTS: Among 451 children, 67 (14.8%) developed hyperglycemia and 27 (6%) progressed to diabetes at a median of 52 days (interquartile range 22-422) from transplant. Multi-organ recipients had a 9-fold [hazard ratio (HR) 8.9; 95% confidence interval (CI) 3.2-25.2] and lung recipients had a 4.5-fold (HR 4.5; 95% CI 1.8-11.1) higher risk for hyperglycemia and diabetes, respectively, compared with kidney transplant recipients. Both magnesium and potassium had modest or no association with the development of hyperglycemia and diabetes. CONCLUSIONS: Hyperglycemia and diabetes occur in 15 and 6% children, respectively, and develop early posttransplant with lung or multi-organ transplant recipients at the highest risk. Hypomagnesemia and hypokalemia do not confer significantly greater risk for hyperglycemia or diabetes in children.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".