The effect of SARS-CoV-2 on the nervous system: a review of neurological impacts caused by human coronaviruses
Bibliographic record
Abstract
The COVID-19 pandemic has affected millions of people worldwide. While coronaviruses typically have low rates of neurotropic effects, the massive transmission of SARS-CoV-2 suggests that a substantial population will suffer from potential SARS-CoV-2-related neurological disorders. The rapid and recent emergence of SARS-CoV-2 means little research exists on its potential neurological effects. Here we analyze the effects of similar viruses to provide insight into the potential effects of SARS-CoV-2 on the nervous system and beyond. Seven coronavirus strains (HCoV-OC43, HCoV-HKU1, HCoV-229E, HCoV-NL63, SARS-CoV, MERS-CoV, SARS-CoV-2) can infect humans. Many of these strains cause neurological effects, such as headaches, dizziness, strokes, seizures, and critical illness polyneuropathy/myopathy. Certain studies have also linked coronaviruses with multiple sclerosis and extensive central nervous system injuries. Reviewing these studies provides insight into the anticipated effects for patients with SARS-CoV-2. This review will first describe the effects of other coronaviruses that have caused severe disease (SARS-CoV, MERS-CoV) on the nervous system, as well as their proposed origins, non-neurological effects, and neurological infection mechanisms. It will then discuss what is known about SARS-CoV-2 in these areas with reference to the aforementioned viruses, with the goal of providing a holistic picture of SARS-CoV-2.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".