The correlation between serum angiopoietin-2 levels and acute kidney injury (AKI): a meta-analysis
Bibliographic record
Abstract
INTRODUCTION: The correlation between serum angiopoietin-2 levels and acute kidney injury (AKI) is a topic of significant clinical interest. This meta-analysis aims to provide a comprehensive evaluation of this relationship. CONTENT: A systematic search was conducted in PubMed, Embase, Web of Science, and Cochrane databases up to October 11, 2023. The included studies were evaluated using the Newcastle-Ottawa Scale (NOS) and Methodological Index for Non-Randomized Studies (MINORS). Weighted mean differences (WMD) and odds ratios (OR) were calculated using random-effects models. Sensitivity analysis, funnel plots, and Egger's test were used to assess the robustness and publication bias of the findings. Subgroup analyses were performed to explore potential variations between adults and children. SUMMARY: Eighteen studies encompassing a total of 7,453 participants were included. The analysis revealed a significant elevation in serum angiopoietin-2 levels in patients with AKI compared to those without (WMD: 4.85; 95 % CI: 0.75 to 0.27; I²=93.2 %, p<0.001). Subgroup analysis indicated significantly higher angiopoietin-2 levels in adults with AKI (WMD: 5.17; 95 % CI: 3.51 to 6.83; I²=82.6 %, p<0.001), but not in children. Additionally, high serum angiopoietin-2 levels were associated with an increased risk of AKI (OR: 1.58; 95 % CI: 1.39 to 1.8; I²=89.1 %, p<0.001). Sensitivity analysis validated the robustness of these results, showing no substantial change in the overall effect size upon the exclusion of individual studies. OUTLOOK: This meta-analysis supports a significant association between elevated serum angiopoietin-2 levels and increased risk of AKI. The observed differential association between adults and children highlights the need for further targeted research to understand these age-specific variations.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.051 |
| Bibliometrics | 0.006 | 0.006 |
| 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".