Neurological and Neuropsychiatric Impacts of COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND: Albeit primarily a disease of respiratory tract, the 2019 coronavirus infectious disease (COVID-19) has been found to have causal association with a plethora of neurological, neuropsychiatric and psychological effects. This review aims to analyze them with a discussion of evolving therapeutic recommendations. METHODS: PubMed and Google Scholar were searched from 1 January 2020 to 30 May 2020 with the following key terms: "COVID-19", "SARS-CoV-2", "pandemic", "neuro-COVID", "stroke-COVID", "epilepsy-COVID", "COVID-encephalopathy", "SARS-CoV-2-encephalitis", "SARS-CoV-2-rhabdomyolysis", "COVID-demyelinating disease", "neurological manifestations", "psychosocial manifestations", "treatment recommendations", "COVID-19 and therapeutic changes", "psychiatry", "marginalised", "telemedicine", "mental health", "quarantine", "infodemic" and "social media". A few newspaper reports related to COVID-19 and psychosocial impacts have also been added as per context. RESULTS: Neurological and neuropsychiatric manifestations of COVID-19 are abundant. Clinical features of both central and peripheral nervous system involvement are evident. These have been categorically analyzed briefly with literature support. Most of the psychological effects are secondary to pandemic-associated regulatory, socioeconomic and psychosocial changes. CONCLUSION: Neurological and neuropsychiatric manifestations of this disease are only beginning to unravel. This demands a wide index of suspicion for prompt diagnosis of SARS-CoV-2 to prevent further complications and mortality.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".