Neurologic Compromise in COVID-19: A Literature Review
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) disease caused by a new coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been associated with many neurological symptoms. The purpose of this article is to describe the neurological manifestations so far reported and their probable pathogenesis. We conducted a literature review on EMBASE, MEDLINE and SCIELO databases using the terms "COVID-19", "COVID", "neurological", "neurologic", "manifestations", "implications", "Guillain-Barre syndrome", "encephalopathy". A total of 33 articles including clinical series, retrospective studies, and case reports were selected and thoroughly reviewed to describe neurological manifestations of COVID-19. There are several neurological manifestations of SARS-CoV-2 infection with different clinical presentations, severity, and prevalence. The most critical ones, such as cerebrovascular disease, encephalopathy, and Guillain-Barre syndrome, were less common and usually associated with previous medical history, known risk factors for cerebrovascular disease or advanced age. The main hypotheses for the spread of the virus are through the hematogenous route or the cribriform plate of the ethmoid bone or a disseminated severe immune response by a cytokine storm. The presence of neurological disturbances associated with laboratory tests alterations is an important clue for the physicians to promptly recognize neurological manifestations 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".