Acute Kidney Injury in Children with Kidney Transplantation
Bibliographic record
Abstract
Background and objectives AKI is associated with progression of CKD. Little is known about AKI after kidney transplantation in pediatric recipients. We aim to describe the epidemiology, risk factors, consequences, and outcomes of AKI in this population. Design, setting, participants, & measurements We performed a retrospective longitudinal analysis of pediatric kidney transplant recipients followed at The Hospital for Sick Children (Toronto, Canada) from 2001 to 2012. AKI was defined as an increase in serum creatinine ≥1.5 times baseline, and a rise of serum creatinine ≥1.25 but <1.5 times baseline defined subacute AKI. Results Of 179 children, 122 were eligible for analysis. At baseline (3 months post-transplant), median age of the children was 13 years old (interquartile range, 9–16 years old), and 53% had CKD stage 2. Congenital anomalies of the kidney and urinary tract accounted for 46% of children. Over the study period (12 years), the incidence of AKI was 37% (n=45 children), and 65% (79 children) experienced subacute AKI. Twenty-seven percent (33 children) did not develop AKI or subacute AKI. The main causes of AKI were infections other than urinary tract infections, rejection, and urinary tract infections. In a multivariable Poisson regression analysis, independent risk factors for AKI included younger age, girls, grafts from deceased donors, and lower baseline eGFR. AKI was significantly associated with lower long-term GFR and graft loss independent of rejection episodes. Moreover, subacute AKI was associated with progression of CKD. Conclusions AKI and subacute AKI were common after pediatric kidney transplantation, and they were associated with graft loss, lower eGFR, and more rapid progression of CKD. Visual Abstract Export
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".