Long Term Renal Outcomes in Survivors of Acute Kidney Injury in Critically-Ill Children and Neonates
Bibliographic record
Abstract
The epidemiology of pediatric acute kidney injury (pAKI) has transformed in the last decades with a shift from primary kidney disease to AKI associated with systemic illnesses or their treatments in critically-ill children and neonates. Even though pAKI has been shown to be associated with poor short-term outcomes including mortality in multiple studies, the long-term renal outcomes in survivors of pAKI in the pediatric and neonatal intensive care unit (PICU/NICU) settings have been understudied. The purpose of this article was to explore the burden of chronic kidney disease (CKD) in survivors of pAKI in critically-ill children and neonates through a review of the literature. We identified 10 observational studies from PICU (n=7) and NICU (n=3) survivors revealing a high prevalence of CKD following AKI (PICU:10-69%, NICU:63-85%). The wide range of CKD prevalence is likely related to multiple sources of heterogeneity between studies including definitions of AKI and CKD, varying lengths of follow-up, and large attrition rates. In light of the large number of patients identified with CKD following pAKI, we suggest that all critically-ill children and neonates should have ongoing surveillance after an AKI episode.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".