Neutrophil to Lymphocyte Ratio as a Predictor of Postoperative Outcomes in Traumatic Brain Injury: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
(1) Introduction: Traumatic brain injury (TBI) is a leading cause of injury and mortality worldwide, carrying an estimated cost of $38 billion in the United States alone. Neutrophil to lymphocyte ratio (NLR) has been investigated as a standardized biomarker that can be used to predict outcomes of TBI. The aim of this review was to determine the prognostic utility of NLR among patients admitted for TBI. (2) Methods: A literature search was conducted in PubMed, Scopus, and Web of Science in November 2022 to retrieve articles regarding the use of neutrophil to lymphocyte ratio (NLR) as a prognostic measure in traumatic brain injury (TBI) patients. Inclusion criteria included studies reporting outcomes of TBI patients with associated NLR values. Exclusion criteria were studies reporting only non-primary data, those insufficiently disaggregated to extract NLR data, and non-English or cadaveric studies. The Newcastle-Ottawa Scale was utilized to assess for the presence of bias in included studies. (3) Results: Following the final study selection 19 articles were included for quantitative and qualitative analysis. The average age was 46.25 years. Of the 7750 patients, 73% were male. Average GCS at presentation was 10.51. There was no significant difference in the NLR between surgical vs. non-surgical cohorts (SMD 2.41 95% CI −1.82 to 6.63, p = 0.264). There was no significant difference in the NLR between bleeding vs. non-bleeding cohorts (SMD 4.84 95% CI −0.26 to 9.93, p = 0.0627). There was a significant increase in the NLR between favorable vs. non-favorable cohorts (SMD 1.31 95% CI 0.33 to 2.29, p = 0.0090). (4) Conclusions: Our study found that NLR was only significantly predictive for adverse outcomes in TBI patients and not surgical treatment or intracranial hemorrhage, making it nonetheless an affordable alternative for physicians to assess patient prognosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.028 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".