The role of the neutrophil-lymphocyte ratio in predicting poor outcomes in COVID-19 patients
Bibliographic record
Abstract
BACKGROUND: This study examines how the neutrophil-lymphocyte ratio (NLR) predicts coronavirus disease 2019 (COVID-19) hospitalization, severity, length, and mortality in adult patients. METHODS: A study was done using a retrospective, single-center, observational design. A total of 400 patients who were admitted to the Ziv Medical Center (Safed, Israel) from April 2020 to December 2021 with a confirmed diagnosis of COVID-19 through RT-PCR testing were included in the analysis. Two complete blood count laboratory tests were conducted for each patient. The first test was administered upon admission to the hospital, while the second test was conducted prior to the patient's discharge from the hospital or a few days before their death. RESULTS: Four hundred patients were included in the study, 206 males (51.5%) and 194 females (48.5%). The mean age was 64.5 ± 17.1 years. In the group of cases, there were 102 deaths, and 296 survivors were recorded, with a fatality rate of 25.5%. The median NLR was 6.9 ± 5.8 at the beginning of hospitalization and 15.1 ± 32.9 at the end of hospitalization (p < 0.001). The median length of hospital stay was 9.4 ± 8.8 days. NLR in the fatality group was 34.0 ± 49.9 compared to 8.4 ± 20.4 in the survivor group (p < 0.001). Comparison between the NLR at the time of admission of the patient and before discharge/death was 6.9 ± 5.8 vs. 15.1 ± 32.9 (p < 0.001). CONCLUSIONS: The analyses conducted revealed a statistically significant correlation between the NLR and the severity, mortality rates, and the duration of hospitalization. The consideration of NLR should commence during the initial phases of the disease when assessing individuals afflicted with COVID-19.
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 | 0.003 |
| 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 | 0.001 |
| 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".