COVID-19 and cerebrovascular diseases: a comprehensive overview
Bibliographic record
Abstract
Neurological manifestations are not uncommon during infection with the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A clear association has been reported between cerebrovascular disease and coronavirus disease 2019 (COVID-19). However, whether this association is causal or incidental is still unknown. In this narrative review, we sought to present the possible pathophysiological mechanisms linking COVID-19 and cerebrovascular disease, describe the stroke syndromes and their prognosis and discuss several clinical, radiological, and laboratory characteristics that may aid in the prompt recognition of cerebrovascular disease during COVID-19. A systematic literature search was conducted, and relevant information was abstracted. Angiotensin-converting enzyme-2 receptor dysregulation, uncontrollable immune reaction and inflammation, coagulopathy, COVID-19-associated cardiac injury with subsequent cardio-embolism, complications due to critical illness and prolonged hospitalization can all contribute as potential etiopathogenic mechanisms leading to diverse cerebrovascular clinical manifestations. Acute ischemic stroke, intracerebral hemorrhage, and cerebral venous sinus thrombosis have been described in case reports and cohorts of COVID-19 patients with a prevalence ranging between 0.5% and 5%. SARS-CoV-2-positive stroke patients have higher mortality rates, worse functional outcomes at discharge and longer duration of hospitalization as compared with SARS-CoV-2-negative stroke patients in different cohort studies. Specific demographic, clinical, laboratory and radiological characteristics may be used as 'red flags' to alarm clinicians in recognizing COVID-19-related stroke.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".