Prognostic Role of Neutrophil to Lymphocyte Ratio in Nonalcoholic Fatty Liver Disease: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction. Nonalcoholic steatohepatitis (NASH) and liver fibrosis are the most common complications of nonalcoholic fatty liver disease (NAFLD). In this systematic review and meta-analysis, we aim to analyze the current literature to evaluate the association of neutrophil to lymphocyte ratio (NLR) with NASH and fibrosis in patients with NAFLD. Methods. PubMed, Web of Science, and Scopus were used to conduct a systematic search for relevant publications published before May 24, 2022. The Newcastle–Ottawa scale was used for quality assessment. Results. Thirteen studies were included in our study. The pooled results showed that NAFLD patients with significant NASH had elevated levels of NLR compared to those with nonsignificant or without NASH (SMD = 0.97, 95% CI = 0.59–1.39, p < 0.001 ). The pooled sensitivity and specificity of NLR were 78.16% (95% CI = 73.70%–82.04%), and 76.93% (95% CI = 70.22%–82.50%), respectively. In addition, NAFLD patients with significant liver fibrosis had elevated levels of NLR compared to those with nonsignificant or without fibrosis (SMD = 1.59, 95% CI = 0.76–2.43, p < 0.001 ). The pooled sensitivity and specificity of NLR were 82.62% (95% CI = 70.235%–90.55%) and 81.22% (95% CI = 75.62%–85.78%), respectively. Conclusion. Our findings support NLR to be a promising biomarker that can be readily integrated into clinical settings to aid in the prediction and prevention of NASH and fibrosis among patients with NAFLD.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".