The neutrophil-to-lymphocyte and monocyte-to-lymphocyte ratios are independently associated with clinical outcomes of viral encephalitis
Bibliographic record
Abstract
Background The neutrophil-to-lymphocyte ratio (NLR) and monocyte-to-lymphocyte ratio (MLR) are used as prognostic biomarkers for many diseases. In this study, we aimed to explore the possibility of using ratios of NLR and MLR to predict the prognosis of viral encephalitis (VE). Methods A total of 81 patients with an initial diagnosis of VE who were admitted to our hospital from January 2018 to January 2021 were retrospectively analyzed. A routine blood test within 24 h of admission was utilized to determine the ratios of NLR and MLR for each patient. The modified Rankin Scale (mRS) at 12 months after discharge was used to evaluate patients' clinical prognosis and the patients were divided into the group of good prognosis (mRS ≤ 1) and the group of poor prognosis (mRS ≥ 2) according to the mRS scores. Univariate and multivariable regression analyses were used to differentiate and assess independent prognostic factors for the prognosis of VE. Results Neutrophil-to-lymphocyte ratio and MLR of the poor prognosis group were significantly higher than that of the good prognosis group. Multivariate logistic regression analysis results showed that NLR [odds ratio (OR): 1.421, 95% confidence interval (CI): 1.105–1.827; P < 0.05] and MLR (OR: 50.423, 95% CI: 2.708–939.001; P < 0.05) were independent risk factors for the poor prognosis of VE. NLR > 4.32 and MLR > 0.44 were suggested as the cutoff threshold for the prediction of the poor prognosis of VE. Conclusion Neutrophil-to-lymphocyte ratio and MLR obtained from blood tests done at hospital admission have the potential to predict poor prognosis in patients with VE.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".