Lymphocyte-to-C-Reactive Protein Ratio: A Novel Predictor of Adverse Outcomes in COVID-19
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Systemic inflammation elicited by a cytokine storm is considered a hallmark of coronavirus disease 2019 (COVID-19). This study aims to assess the validity and clinical utility of the lymphocyte-to-C-reactive protein (CRP) ratio (LCR), typically used for gastric carcinoma prognostication, versus the neutrophil-to-lymphocyte ratio (NLR) for predicting in-hospital outcomes in COVID-19. METHODS: -test and multivariate logistic regression analysis were performed to calculate mean differences and adjusted odds ratios (aORs) with its 95% confidence interval (CI), respectively. RESULTS: The mean age for NLR patients was 63.6 versus 61.6, and for LCR groups, it was 62.6 versus 63.7 years, respectively. The baseline comorbidities across all groups were comparable except that the higher LCR group had female predominance. The mean NLR was significantly higher for patients who died during hospitalization (19 vs. 7, P ≤ 0.001) and those requiring IMV (12 vs. 7, P = 0.01). Compared to alive patients, a significantly lower mean LCR was observed in patients who did not survive hospitalization (1,011 vs. 632, P = 0.04). For patients with a higher NLR (> 10), the unadjusted odds of mortality (odds ratios (ORs) 11.0, 3.6 - 33.0, P < 0.0001) and need for IMV (OR 3.3, 95% CI 1.4 - 7.7, P = 0.008) were significantly higher compared to patients with lower NLR. By contrast, for patients with lower LCR (< 100), the odds of in-hospital all-cause mortality were significantly higher compared to patients with a higher LCR (OR 0.2, 0.06 - 0.47, P = 0.001). The aORs controlled for baseline comorbidities and medications mirrored the overall results, indicating a genuinely significant correlation between these biomarkers and outcomes. CONCLUSIONS: A high NLR and decreased LCR value predict higher odds of in-hospital mortality. A high LCR at presentation might indicate impending clinical deterioration and the need for IMV.
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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 | 0.012 | 0.146 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it