Challenges in pediatric transplantation: The impact of chronic kidney disease and cardiovascular risk factors on long‐term outcomes and recommended management strategies
Bibliographic record
Abstract
Barriers to successful outcomes following pediatric transplantation have shifted from ischemic reperfusion injury and rejection to more long-term complications. Of particular concern is the high prevalence of CKD owing to preexisting damage and nephrotoxicity, as well as other CV complications such as hypertension and cardiomyopathy. All of these contribute to graft loss and shortened life expectancy, thereby limiting the success story of solid-organ transplantation. Managing CKD and related CV morbidity should be integral to the care of pediatric transplant patients, and timely detection of any irregularities would increase the chances of restoring lost kidney function. GFR is still the widely accepted indicator of renal function, and nuclear medicine techniques are the gold standard measurement methods. These methods are limited by costs, radiation exposure and substrate injection, and current practice still uses the Schwartz estimate, despite its well-documented limitations. Newer endogenous markers of GFR, such as cystatin C clearance, give a more accurate measure of true GFR but have not been embraced in the management of pediatric transplant recipients. Furthermore, indirect markers (e.g., microalbuminuria and hypertension) could also aid early detection of renal damage. The effects of mainstay immunosuppressants on kidney and heart function are varied, with available data indicating favorable outcomes with tacrolimus compared with ciclosporin. There is a need for appropriately designed and powered randomized controlled trials to validate innovative concepts for tailored immunosuppression in the pediatric population. To date, very few studies have generated long-term data in pediatric renal transplant patients - results of 1-4-yr study favored tacrolimus over ciclosporin, but other immunosuppressive agents also need to be evaluated.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".