Neurological Manifestations of SARS-CoV-2
Bibliographic record
Abstract
BACKGROUND: Coronavirus disease 2019 (COVID-19) pandemic started as an outbreak in China and soon crossed borders to affect the populations in all countries of the world. During the initial course of the disease, COVID-19 was perceived as a pneumonia-like illness. However, recent findings of COVID-19 patients suggest that the virus has the potential to disseminate to different tissues and organs, and cause significant complications. SUMMARY: Neurological symptoms are of great significance as these usually present in and complicate critical cases. Many case reports and case series have documented the findings of neurological complications in COVID-19 patients. From the existing data, the most frequent symptoms in these patients were broadly classified into the central nervous system (CNS), peripheral nervous system, and skeletal muscular symptoms. CNS symptoms include meningitis, encephalitis, cerebrovascular complications, peripheral nervous system symptoms include anosmia, ageusia, and skeletal muscular symptoms include myalgias. It is postulated that the cause may be direct CNS injury through blood and neuronal pathways or indirectly because of an immune-mediated response, hypoxia caused by decreased oxygen saturation, or by the binding of subacute respiratory syndrome-coronavirus-2 to the host angiotensin-converting enzyme-2 receptors. Striking radiologic findings in COVID-19 patients with neurological symptoms have also emerged. CONCLUSIONS: As subacute respiratory syndrome-coronavirus-2 may potentially have lethal implications on the nervous system, it is important that neurologists are better informed about the spectrum of clinical manifestations, radiologic findings, and likely mechanisms of injury. Understanding the symptoms and radiologic imaging allows clinicians to consider brain imaging in any patient with suspected COVID-19 and neurological symptoms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".