Incidence and risk factors for cardiovascular events and death in pediatric renal transplant patients: A single center long‐term outcome study
Bibliographic record
Abstract
There are considerable mortality data associated with renal transplantation in children; however, morbidity data, especially related to CV disease, are scarce. The objectives of this study were to determine incidence of non-fatal and fatal CV events and all-cause mortality in PRTx and evaluate risk factors for these conditions. Using a population-based retrospective cohort design, 274 PRTx with or without a functioning graft was followed until death or date of last contact (median follow-up 11.9 yr). Primary outcomes (time to first fatal or non-fatal CV event and all-cause mortality after first transplant) were ascertained from chart review and linkage with administrative databases of a universal health care system. During 3073 patient-years, there were 46 deaths; 13 were because of CV disease. Twenty patients had CV events that did not result in death. Post-transplant diabetes mellitus (10.5%) was associated with increased risk of death (HR: 2.79, 95% CI: 1.04-7.44) and CV events (HR: 3.90, 95% CI: 1.31-11.59). Low estimated glomerular filtration rate at one yr post-transplant was also associated with increased risk of death. The rates of developing CV disease and dying prematurely are extraordinarily high in PRTx, underscoring the need for early and aggressive intervention to reduce the burden of suffering in this patient population.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".