Neutrophil‐To‐Lymphocyte Ratio as a Prominent Systemic Inflammatory Indicator of Poor Functional Outcomes in Cerebral Venous Thrombosis: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
ABSTRACT Cerebral venous thrombosis (CVT) is considered a form of venous thromboembolism, with potential inflammatory pathways that lead to the formation of blood clotting and subsequent neurological damage. This systematic review and meta‐analysis synthesized the current evidence on systemic inflammatory biomarkers related to poor functional outcomes in patients with CVT. A comprehensive search strategy was conducted based on the PRISMA statement across seven databases, including PubMed/Medline, Scopus, EBSCOhost, Web of Science, Cochrane Library, CNKI, and Wanfang, covering literature up to December 21, 2024. The methodological quality of included studies was assessed using the Newcastle–Ottawa Scale. Twenty studies involved 4330 participants (65.91% female) for final analysis. The modified Rankin Scale (mRS) was used to evaluate functional outcomes. Sixty‐five percent of studies ( n = 13) defined poor outcomes as mRS ≥ 3. Eighty‐five percent of the studies achieved a score of 7–9/9 on the Newcastle–Ottawa Scale. The neutrophil‐to‐lymphocyte ratio (NLR) was the most frequently reported biomarker ( n = 9) among five others (ANC, ALC, WBC, CRP, and infection), showing a significant association with poor outcomes (InOR = 1.76, 95% CI: 0.95–2.97, p = 0.000). Meta‐regression revealed that the NLR increased with higher mRS scores (Wald χ 2 , p < 0.001), and younger age showed stronger modulation (Wald χ 2 , p = 0.033). This systematic review provides a comprehensive overview of the role of systemic inflammation in predicting functional outcomes in patients with CVT and provides potential targets for future clinical 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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.007 | 0.007 |
| 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) | 0.004 | 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".