The Neutrophil to Lymphocyte Ratio in Poststroke Infection: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Ischemic and hemorrhagic strokes have multiple downstream consequences for patients. One of the most critical is poststroke infection (PSI). The goal of this systematic review and meta-analysis was to critically evaluate the literature regarding the use of the neutrophil to lymphocyte ratio (NLR) as a reliable means to detect early PSI development, particularly poststroke pneumonia (PSP) development to help clinicians institute early interventions and improve outcomes. The following were the inclusion criteria: (1) cross-sectional, case-control, and cohort studies; (2) studies comparing NLR data from PSI or PSP patients to controls; and (3) studies with a control group of stroke patients without infection. There was not any language or publication preference. The Newcastle-Ottawa Scale was used by two writers to assess the quality of the included studies. We assessed the certainty of the associations with GRADE methods. Web of Science, PubMed, and Scopus were searched, and 25 studies were included in the qualitative review. Among them, 15 studies were included in the meta-analysis. Standardized mean difference (SMD) was reported with a 95% confidence interval (CI) for the NLR levels. Patients with PSI had significantly higher NLR levels than stroke patients without infection ( SMD = 1.08 ; CI 95 % = 0.78 ‐ 1.39 , P value < 0.001). In addition, the NLR levels of the stroke patients with pneumonia were significantly higher than those without pneumonia ( SMD = 0.98 ; CI 95 % = 0.81 ‐ 1.14 , P value < 0.001). However, data extracted from the qualitative review suggested that NLR could not predict urinary tract infection, sepsis, or ventriculitis in stroke patients. Our study indicated that NLR could be recommended as an inexpensive biomarker for predicting infection, particularly pneumonia, in stroke patients. It can help clinicians institute early interventions that can reduce PSI and improve outcomes.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.038 |
| Bibliometrics | 0.008 | 0.009 |
| 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".