Impact of COVID-19 on Neurological Manifestations: An Overview of Stroke Presentation in Pandemic.
Bibliographic record
Abstract
Abstract Introduction: Corona virus disease 2019 (COVID-19) pandemic has become a globally challenging issue after its emergence in December 2019 from Wuhan, China. Despite its common presentation as respiratory distress, patients with COVID-19 have also shown neurological manifestation especially stroke. Therefore, the authors sought to determine the etiology, underlying risk factors, and outcomes among patients with COVID-19 presenting with stroke. Methods: We conducted a systematic review of the electronic database (PubMed, Google Scholar, Scopus, Medline, EMBASE, and Cochrane library) using different MeSH terms from January 2000 to June 2020. Results: A total of 39 patients with stroke from 6 studies were included. The mean age of our included patients was 61.4±14.2 years. Majority of the patients (92.3%) with COVID-19 had ischemic stroke, 5.1% had hemorrhagic stroke, and 2.6% had cerebral venous thrombosis at the time of initial clinical presentation. Almost all of the patients presented had underlying risk factors predisposing to stroke which included, diabetes mellitus, hyperlipidemia, hypertension, and previous history of cerebrovascular disease. 51.2% of the included patients infected with COVID-19 with stroke died, while remaining patients were either discharged home or transferred to a rehabilitation unit. Conclusion: Exploring the neurological manifestation in terms of stroke among patients with COVID-19 is a step towards better understanding of the virus, preventing further spread, and treating the patients affected by this pandemic.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".