Sex- and gender-specific differences in the inflammatory response to COVID-19: the role of the neutrophil-to-lymphocyte ratio
Bibliographic record
Abstract
INTRODUCTION: The neutrophil-to-lymphocyte ratio (NLR) is a simple blood test marker used to predict the severity of COVID-19. The study aimed to describe the correlation between neutrophil–to–lymphocyte ratio sex- and gender-specific differences in the inflammatory response to COVID-19. MATERIAL AND METHODS: This retrospective observational study was conducted with patients diagnosed with COVID-19 in the emergency department of a tertiary hospital between January 1, 2022, and May 31, 2022. The receiver operating characteristic (ROC) area under the curve (AUC) analysis was conducted to verify NLR predictive capacity. RESULTS: The study population consisted of 47% women and 53% men with a mean age of 72.42 years. Women were significantly older than men on average. At admission, 73% of patients were classified as nonsevere, while 27% were severe. Overall, 63% of patients survived the infection. CONCLUSIONS: There were slight but not statistically significant differences in neutrophil counts between men and women. However, there were significant differences in lymphocyte counts and the NLR, with women having higher lymphocyte counts and men having higher NLR. The study found very weak correlations between age and neutrophil counts, lymphocyte counts, and NLR, suggesting no strong relationship between age and these variables. Patients with severe disease had higher neutrophil counts and NLR but lower lymphocyte counts compared to nonsevere patients. Survivors had lower neutrophil counts and NLR but higher lymphocyte counts compared to those who did not survive. NLR was a significant predictor of both admission status and survivor status, with ROC AUC values indicating its predictive capacity. These findings highlight the potential importance of NLR as a biomarker in predicting disease severity and survival in patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".