Long-Term Outcomes After Pediatric Non-Dialysis-Treated AKI: A Population-Based Cohort Study
Bibliographic record
Abstract
Background: Acute kidney injury (AKI) is common in hospitalized children. Dialysis-treated pediatric AKI is associated with long-term chronic kidney disease (CKD), hypertension, and death. We aim to evaluate the outcomes after non-dialysistreated AKI, which are uncertain. Methods: Retrospective cohort study of all hospitalized children (0-18yr) surviving non-dialysis-treated AKI from 1996-2020 in Ontario, identified via provincial administrative health databases. Children with prior kidney replacement therapy (KRT; dialysis or transplant), CKD, or AKI were excluded. Cases were matched with up to four hospitalized controls without AKI by age, neonatal status, sex, index year, ICU admission, cardiac surgery, malignancy, hypertension, and a propensity score for AKI. Children were followed until death (2.9%), provincial emigration (5.3%), or March 2021 (91.8%). The primary outcome was major adverse kidney events (MAKE; composite of death, chronic KRT, or de novo CKD). Results: A total of 4173 pediatric AKI survivors were matched to 16,337 hospitalized controls. Baseline covariates were well-balanced after propensity score matching. Median age was 8yr (IQR 1-15); 706 (16.9%) AKI cases were neonates. During median 9.7-year follow-up, 17.6% of AKI survivors developed MAKE vs 4.6% of controls (HR 4.3, 95%CI 3.9-4.8, p<0.001). AKI cases had higher rates of chronic KRT (2.2% vs 0.2%; HR 12.8, 95%CI 8.5-19.4), CKD (15.9% vs 2.0%; HR 8.8, 95%CI 7.7-10.0), hypertension (16.8% vs 7.7%; HR 2.4, 95%CI 2.2-2.7), and subsequent AKI (5.7% vs 1.5%; HR 4.0, 95%CI 3.4-4.8), but no mortality difference (2.7% vs 2.9%; HR 1.0, 95%CI 0.78-1.16). Conclusions: Children with non-dialysis-treated AKI are at increased long-term risk of CKD, chronic KRT, hypertension, and subsequent AKI vs hospitalized controls. Funding: Private Foundation SupportFigure.: Cumulative incidence of MAKE
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".