Association between neutrophil to lymphocyte ratio and blood glucose level at admission in patients with spontaneous intracerebral hemorrhage
Bibliographic record
Abstract
Previous studies indicated that both inflammatory responses and hyperglycemia are involved in the similar pathophysiological mechanisms after onset of intracerebral hemorrhage (ICH). However the relationship between hyperglycemia and inflammation remains unknown. We aim to evaluate the associations of hyperglycemia with inflammation and neutrophil to lymphocyte ratio (NLR) in patients with ICH. Patients with acute ICH were retrospectively enrolled. Clinical characteristics and imaging features were obtained. The associations between outcome and laboratory biomarkers were assessed by multivariable logistic regression analysis. Spearman analysis and multiple linear regression analysis were performed to estimate the association of NLR and serum glucose. 175 patients were enrolled. Poor outcome occurred in 86 patients at 30 days. Elevated blood glucose level (BGL) and NLR were strongly associated with outcome in patients with ICH. Moreover, combined NLR-BGL exhibited a better predictive accuracy compared with the peripheral leukocyte counts. Furthermore, there was a robust association between BGL and NLR. We first demonstrated both of NLR and BGL were independently associated with each other. Our results indicate that inflammatory responses and the pathological process of hyperglycemia may influence each other by several complex pathological mechanisms and have a mutual promoting effect to secondary brain injury.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".