Bibliographic record
Abstract
This review summarizes the focus shift from ischemia-reperfusion injury and avoidance of rejection to long-term outcome after pediatric renal transplantation over the past decade. Although there has been excellent 1-year graft and patient survival, low rejection rates can be achieved with modern immunosuppression after pediatric renal transplantation, and patient survival is improved substantially in comparison with dialysis, pediatric renal transplant recipients experience a high prevalence of infections, malignancies, medication side effects, nonadherence, and, most importantly, cardiovascular morbidity and mortality. Additional challenges occur because of a high prevalence of obesity after transplantation and vascular calcifications. There is also in an underappreciation of chronic kidney disease (CKD) in transplant recipients. The etiology of CKD is multifactorial and can affect graft and patient survival. The rigors of treatment for CKD are less compared with CKD in nontransplant recipients. Almost all immunosuppressive drugs are implicated with a risk of hypertension, hyperlipidemia, and diabetogenicity, all of which contribute to cardiovascular morbidity. Corticosteroids exhibit the most substantial risk and also stunt growth. Effective new treatment protocols such as the recent European Tacrolimus and WIthdrawal of STeroids (TWIST) study with rapid steroid withdrawal after 5 days provide promising results without increasing the rejection risk. The shift in focus on long-term complications allows for improved graft outcome. Side effects of immunosuppressive medications require continued attention to further improve long-term outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".