Urinary KIM-1 for Early Detection of Acute Kidney Injury in Neonates: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Acute kidney injury (AKI) is a significant clinical concern in neonates, threatening optimal outcomes. Early and accurate diagnosis is crucial; however, current methods lack sufficient sensitivity. This meta-analysis aimed to evaluate urinary kidney injury molecule-1 (uKIM-1) for AKI in neonates by quantifying differences in uKIM-1 levels between AKI and non-AKI neonates. We systematically searched major databases for comparative studies. Quality assessment was performed using the Newcastle-Ottawa Scale, and the certainty of the evidence was assessed according to the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology. A random-effects meta-analysis estimated the pooled Hedges’ g in uKIM-1 levels, accounting for heterogeneity. Subgroup analyses explored sources of heterogeneity (continent, study design, sampling time, AKI definition). Publication bias was assessed using Egger’s and Begg’s tests, as well as with a funnel plot. Data from 13 studies involving 552 neonates indicated a significant association between elevated uKIM-1 levels and AKI. High heterogeneity was observed (I2 = 80.32%). The pooled Hedges’ g was 0.62 (95% CI: 0.16–1.07, p = 0.01). Subgroup analysis showed stronger associations in African studies (Hedges’ g = 2.12), those using KDIGO (Hedges’ g = 0.96), cohort studies, and sampling within 2–4 days (Hedges’ g = 0.76). No publication bias was detected. This meta-analysis synthesizes evidence on uKIM-1 as an AKI biomarker. While uKIM-1 shows promise, high heterogeneity and diagnostic performance warrant further research to improve AKI detection and management in neonates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".