The Diagnostic and Predictive Role of Neutrophil-Lymphocyte Ratio, Lymphocyte-Monocyte Ratio, Platelet-Lymphocyte Ratio and C-Reactive Protein in Diabetic and Nondiabetic COVID-19 Patients
Bibliographic record
Abstract
Background: Coronavirus disease 2019 (COVID-19) infection is more severe in diabetic cases due to abnormality in hematological and inflammatory markers. This study was conducted to determine the values of neutrophil-lymphocyte ratio (NLR), lymphocyte-monocyte ratio (LMR), platelet-lymphocyte ratio (PLR) and C-reactive protein in COVID-19 diabetic and COVID-19 nondiabetic patients, with a specific focus on associating these markers with disease severity and mortality. Methods: A descriptive study was done by collecting hematological and inflammatory laboratory parameters of COVID-19 diabetic patients (n = 123) and COVID-19 nondiabetic patients (n = 124) retrospectively at King Fahad Medical City, Saudi Arabia. Results: Compared with nondiabetics, patients with diabetes were older, and their mean values of white blood cells (9.16; 8.22), monocytes (7.68; 7.08), and eosinophils were high (0.69; 0.26), and lymphocytes were low (17.65; 18.77). The NLR, LMR, PLR, C-reactive protein and D-dimer were higher, with statistical significance for NLR (P = 0.05) and PLR (P = 0.005). Diabetic COVID-19 cases had longer hospital stay (17 days), higher intensive care admissions (28.5%), and a higher mortality rate (11.4%). The percentage of diabetic COVID-19 patients with comorbidities was higher. Multinomial logistic regression analysis was performed controlling for age and sex, and we obtained odds ratio for several factors. The association for NLR, LMR, PLR and D-dimer with mortality and severity was not statistically significant. Conclusions: The results obtained from this research identified that NLR, LMR, PLR, C-reactive protein, and D-dimer were higher in COVID-19 diabetic patients than COVID-19 nondiabetic patients. J Endocrinol Metab. 2024;14(2):71-77 doi: https://doi.org/10.14740/jem934
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 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 | 0.001 | 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".