Incidence of Acute Kidney Injury in Hospitalized Children: A Meta-analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: There is limited literature on the incidence of acute kidney injury (AKI) and associated mortality in hospitalized children. To systematically assess the worldwide incidence of AKI in hospitalized children to inform policymakers regarding appropriate health resource allocation. METHODS: Three different databases were searched (PubMed, Embase, Web of Sciences) from March 2012 to January 2022 without language or geographical restrictions. We included cohort and cross-sectional studies that reported AKI incidence in hospitalized children. Eligible studies had at least 100 participants and used the standard Kidney Disease Improving Global Outcomes criteria to define AKI. Two authors extracted data on the study and patients' characteristics and outcomes (incidence and AKI-associated mortality) and performed the risk of bias assessment. We used a random-effects meta-analysis to generate pooled estimates. RESULTS: We included 94 studies (202 694 participants) from 26 countries. The incidence of any AKI was 26% (95% confidence interval: 22-29), and that of moderate-severe AKI was 14% (11-16). The incidence of AKI was similar in high-income 27% (23-32), low-middle-income 25% (13-38), and low-income 24% (12-39) countries. Overall, AKI-associated mortality was observed in 11% (9-13) of the pediatric population. AKI-associated mortality rate was highest at 18% (11-25) and 22% (9-38) in low-income and low-middle-income countries, respectively. CONCLUSIONS: AKI was observed in one-quarter of the hospitalized children and is associated with increased mortality risk. Low-income and low-middle-income countries had observed higher mortality rates compared with high-income countries despite a similar AKI burden.
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.071 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".