Neutrophil-to-lymphocyte ratio (NLR) as a prognostic marker for Dengue severity: A systematic review and meta analysis
Bibliographic record
Abstract
Background: Dengue fever, a viral infection transmitted by Aedes mosquitoes, manifests in a spectrum of clinical presentations, ranging from mild to severe disease. The neutrophil-to-lymphocyte ratio (NLR), a marker of systemic inflammation, has been investigated as a potential prognostic indicator for dengue severity. However, its predictive value remains inconclusive due to inconsistent evidence. Methods: This systematic review and meta-analysis adhered to the PRISMA guidelines. Relevant observational studies were retrieved from ScienceDirect, Web of Science, Scopus, NCBI PubMed, ProQuest, and EBSCO databases. Studies reporting NLR values and their association with dengue severity were included. Quality assessment was performed using the Newcastle-Ottawa Scale, and data analysis employed a random-effects model to address heterogeneity. Results: A total of nine studies involving 3,289 participants were included. Findings regarding the relationship between NLR and dengue severity were inconsistent. The results of this meta-analysis highlight a significant correlation between a higher NLR and the severity of dengue fever (OR 2.74 (95% CI: 1.97, 3.81)). Conclusions: The NLR demonstrates potential as a simple and cost-effective inflammatory marker; however, its prognostic utility in dengue severity remains limited. The overall meta-analysis suggests a significant correlation between elevated NLR and increased severity of dengue infection. However, NLR should be considered alongside other clinical indicators to provide a more comprehensive assessment of dengue severity. Future research with larger and more homogeneous sample sizes is necessary to further validate these findings and explore the underlying mechanisms linking NLR to disease progression.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".