Neutrophil to Lymphocyte Ratio as a Biomarker for Predicting the Coronary Artery Abnormality in Kawasaki Disease: A Meta-Analysis
Bibliographic record
Abstract
We conducted a systematic review and meta-analysis on the relationship between the neutrophil to lymphocyte ratio (NLR) and coronary artery abnormalities (CAA) in patients with Kawasaki disease (KD), according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statements. We searched PubMed, Scopus, Web of Science, Embase, TRIP, Google Scholar, and ProQuest up to the 8th of August 2022. This was done to retrieve eligible studies. No date or language limitations were considered in this study. Methodology quality assessment was conducted according to the Newcastle–Ottawa scale (NOS). Standard mean difference (SMD) and its 95% confidence interval (CI) were used to depict the pooled continuous variables. Finally, 17 articles with 6334 KD patients, of whom 1328 developed CAA, were enrolled in this meta-analysis. NLR level was significantly higher in KD patients with CAA compared to those without CAA (SMD =0.81; 95% CI =0.05–1.57, P=0.03 ). In addition, NLR level was significantly higher in patients with coronary artery aneurysms than those without coronary artery aneurysms (SMD =2.29; 95% CI =0.18–4.41, P=0.03 ). However, no significant association between NLR and coronary artery dilation was observed in this meta-analysis (SMD =0.56; 95% CI = -0.86–1.99). There was no publication bias for the pooled SMD of NLR for coronary artery abnormality in KD (Egger’s test P=0.82 ; Begg’s test P=0.32 ). The NLR may be useful in monitoring CAA development in these patients and may further imply a mechanistic role in potential inflammation that mediates this process.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.056 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".