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: < 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.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".