Neutrophil-lymphocyte ratio as a predictor of outcome following traumatic brain injury: Systematic review and meta-analysis
Bibliographic record
Abstract
Objectives: The neutrophil-to-lymphocyte ratio (NLR) is a simple and routinely performed hematological parameter; however, studies on NLR as a prognostic tool in traumatic brain injury (TBI) have yielded contradictory results. Materials and Methods: This systematic review and meta-analysis was conducted according to the Preferred Reporting Items in the Systematic Review and Meta-Analysis guidelines 2020. Electronic databases of PubMed, Cochrane Library, Web of Science, and Scopus were searched. The population consisted of TBI patients in the absence of moderate and severe extracranial injury. Day 1 NLR was taken for the analysis. The outcomes evaluated were mortality and the Glasgow Outcome Scale (GOS). No restrictions were placed on the language, year and country of publication, and duration of follow-up. Animal studies were excluded from the study. Studies, where inadequate data were reported for the outcomes, were included in the qualitative synthesis but excluded from the quantitative synthesis. Study quality was evaluated using the Newcastle-Ottawa scale (NOS). The risk of bias was estimated using the Cochrane RoBANS risk of bias tool. Results: We retrieved 7213 citations using the search strategy and 2097 citations were excluded based on the screening of the title and abstract. Full text was retrieved for 40 articles and subjected to the eligibility criteria, of which 28 were excluded from the study. Twelve studies were eligible for the synthesis of the systematic review while seven studies qualified for the meta-analysis. The median score of the articles was 8/9 as per NOS. The risk of selection bias was low in all the studies while the risk of detection bias was high in all except one study. Ten studies were conducted on adult patients, while two studies reported pediatric TBI. A meta-analysis for GOS showed that high NLR predicted unfavorable outcomes at ≥6 months with a mean difference of −5.18 (95% confidence interval: −10.04, −0.32); P = 0.04; heterogeneity (I2), being 98%. The effect estimates for NLR and mortality were a mean difference of −3.22 (95% confidence interval: −7.12, 0.68), P = 0.11, and an I2 of 85%. Meta-analysis for Area under the curve (AUC) receiver operating characteristic of the included studies showed good predictive power of NLR in predicting outcomes following TBI with AUC 0.706 (95% CI: 0.582–0.829). Conclusion: A higher admission NLR predicts an increased mortality risk and unfavorable outcomes following TBI. However, future research will likely address the existing gaps.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.035 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".