Correlation between increased neutrophil gelatinase-associated lipocalin urine protein level with disease activities on pediatrics lupus nephritis: A meta-analysis study
Bibliographic record
Abstract
This study aims to evaluate the correlation between urinary Neutrophil Gelatinase-Associated Lipocalin (NGAL) levels and disease activity in pediatric lupus nephritis, emphasizing its role as a potential biomarker. A systematic review and meta-analysis were conducted on observational studies (cross-sectional, cohort, and case-control) sourced from PubMed, Science Direct, Scopus, and Web of Science, covering publications up to March 2024. The Newcastle-Ottawa Scale was used to assess study quality, and data analysis was performed using Review Manager 5.4 and STATA SE 16. Pooled correlation values were calculated using a fixed-effects model. The meta-analysis included nine studies involving pediatric patients. Results indicated a significant positive correlation between urinary NGAL levels and lupus nephritis disease activity on pediatrics, with a pooled Fisher's z of 0.4 (95% CI 0.34–0.46) and a pooled correlation coefficient of 0.380 (95% CI 0.327–0.430; p < 0.05). Publication bias was minimal, as shown by Egger’s test (p > 0.05) and a symmetrical funnel plot. Sensitivity analysis confirmed the robustness of the findings. The study demonstrates that urinary NGAL is a reliable biomarker for assessing disease activity in pediatric lupus nephritis, offering a non-invasive method for monitoring disease progression. These findings have significant clinical relevance, supporting the integration of urinary NGAL measurement into routine practice for early detection and management of lupus nephritis flares in children, thereby improving patient outcomes through timely intervention.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.045 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".