Prevalence of renal dysfunction in tacrolimus‐treated pediatric transplant recipients: A systematic review
Bibliographic record
Abstract
Renal dysfunction after non-renal transplantation in adult tacrolimus-treated transplant patients is well documented. Little is known about its prevalence in children. Age-related changes in both disposition and effect of tacrolimus as well as renal function may preclude extrapolation of adult data to children. To systematically review the literature on renal dysfunction in non-renal pediatric transplant recipients treated with tacrolimus. PubMed/Medline, Embase, and Google were searched from their inception until April 19, 2012, with the search terms "tacrolimus," "renal function," "transplantation," and "children." Eighteen of 385 retrieved papers were considered relevant. Twelve dealt with liver, four with heart transplant, one with heart and lung transplant, and one with intestinal recipients. Reported prevalences of mild and severe chronic kidney disease ranged from 0% to 39% and 0% to 71.4%, respectively, for liver, and from 22.7% to 40% and 6.8% to 46%, respectively, for heart and/or lung transplant recipients. Ranges remained wide after adjusting for follow-up time and disease severity. Possible explanations are inclusion bias and definitions used for renal dysfunction. A considerable proportion of pediatric non-renal transplant patients who receive tacrolimus-based immunosuppression, appear to suffer from chronic kidney disease. This conclusion warrants further research into the real risk, its risk factors, and individualization of immunosuppressant therapy.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".