Neurological Presentations in Patients with COVID-19 in Cytokine Storm
Bibliographic record
Abstract
Abstract: Background: Coronavirus disease 2019 (COVID-19) infection causes a wide variety of neurological disorders by affecting both central and peripheral nervous systems. The cytokine storm (CS) has been blamed for the development of severe neurological disorders in COVID-19. However, the relationship between COVID-19 CS and neurological manifestations has not been adequately studied. Thus, we aimed to investigate the neurological presentations in patients with COVID-19 CS. Methods: The study population consisted of hospitalized moderate-to-severe COVID-19 patients. It was divided into two groups CS (36 patients, 29.3%) and non-CS (87 patients, 70.7%) based on significant clinical symptoms, elevated inflammatory marker levels, radiological findings, and interleukin-6 levels (IL-6). Results: The three most common neurological symptoms in the CS group were altered level of consciousness, headache, and unsteadiness. Altered level of consciousness was higher in the CS group (69.4%) than the non-CS group (25.3%) (p:0.001). The frequency of headache was comparable in both groups (p:0.186). The number of patients requiring intensive care unit and intubation was higher in the CS group (p:0.005 and p:0.001). The mortality rate in the CS group (38.9%) was higher than the non-CS group (8.0%) (p:0.001). IL-6, CRP, ferritin, neutrophil-lymphocyte ratio, procalcitonin, and D-dimer levels were higher in the CS group (for all p:0.001) while lymphocyte count was lower (p:0.003). Conclusion: The most common neurological presentation in patients with CS was altered level of consciousness. The presence of CS was an independent risk factor for high mortality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".