The prognostic value of neutrophil-to-lymphocyte ratio in patients with traumatic brain injury: A systematic review
Bibliographic record
Abstract
Traumatic brain injury (TBI) places a heavy load on healthcare systems worldwide. Despite significant advancements in care, the TBI-related mortality is 30–50% and in most cases involves adolescents or young adults. Previous literature has suggested that neutrophil-to-lymphocyte ratio (NLR) may serve as a sensitive biomarker in predicting clinical outcomes following TBI. With conclusive evidence in this regard lacking, this study aimed to systematically review all original studies reporting the effectiveness of NLR as a predictor of TBI outcomes. A systematic search of eight databases was conducted according to the Preferred Reporting Items for Systematic Review and Meta-Analyses statement (PRISMA) recommendations. The risk of bias was assessed using the Quality in Prognostic Studies (QUIPS) tool. Eight studies were ultimately included in the study. In most of the studies interrogated, severity outcomes were successfully predicted by NLR in both univariate and multivariate prediction models, in different follow-up durations up to 6 months. A high NLR at 24 and 48 h after TBI in pediatric patients was associated with worse clinical outcomes. On pooling the NLR values within studies assessing its association with the outcome severity (favorable or not), patients with favorable outcomes had 37% lower NLR values than those with unfavorable ones (RoM= 0.63; 95% CI = 0.44–0.88; p = 0.007). However, there were considerable heterogeneity in effect estimates (I2 = 99%; p < 0.001). Moreover, NLR was a useful indicator of mortality at both 6-month and 1-year intervals. In conjunction with clinical and radiographic parameters, NLR might be a useful, inexpensive marker in predicting clinical outcomes in patients with TBI. However, the considerable heterogeneity in current literature keeps it under investigation with further studies are warranted to confirm the reliability of NLR in predicting TBI 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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".