Role of the Neutrophil to Lymphocyte Ratio in Guillain Barré Syndrome: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
In this study, we conducted a systematic review and meta-analysis regarding the role of the neutrophil to lymphocyte ratio (NLR) in Guillain Barré syndrome (GBS). The most recent update to the search was on July 18, 2022, through the databases of Web of Science, PubMed, Embase, and Scopus. The Newcastle-Ottawa scale was used for quality assessment of included studies. Finally, 14 studies were included in the review, and among them, ten studies were included in the meta-analysis. Our results showed that NLR levels were significantly increased in the patients with GBS compared with healthy controls ( SMD = 1.05 ; 95 % CI = 0.59 to 1.50, P < 0.001 ). After treatment, NLR levels were decreased to the extent that they became similar to healthy controls ( SMD = − 0.03 , 95 % CI = − 0.29 to 0.22, P = 0.204 ). Moreover, NLR was a stable predictor of outcome or response to treatment in such patients ( SMD = 1.01 , 95 % CI = 0.65 to 1.37, P < 0.001 ); the higher the NLR, the worse the outcome. In addition, patients who underwent mechanical ventilation had higher levels of NLR compared to those who did not ( SMD = 0.93 , 95 % CI = 0.05 to 1.82, P = 0.03 ). However, NLR levels were not different among distinct GBS subtypes, so it could not distinguish among them. In conclusion, our analysis indicates that the NLR levels are highly elevated in patients with GBS. Therefore, the NLR has the potential to be used as a biomarker to inform diagnosis, prognosis, or treatment responses in GBS, and future studies are warranted.
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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 | 0.025 | 0.053 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.044 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".