COVID-19 and Its Long-Term Neurological and Cognitive Implications: A Literature Review
Bibliographic record
Abstract
Introduction: COVID-19 is an infectious disease resulting from severe acute respiratory syndrome. Individuals with prior COVID-19 infection have had neurological complaints of impaired attention, fatigue, and “brain fog”. This review seeks to summarize the associations between COVID-19 infection and the development of the neurocognitive elements of post-acute COVID syndrome (PACS) which is relevant to healthcare workers for the efficient treatment and management of the long-term effects of COVID. Methods: Literature that examines the neurocognitive complaints caused by COVID-19 infection, including brain fog, attention deficits, psychiatric impairment, and fatigue were selected. Google Scholar and PubMed were the primary databases used to obtain relevant literature. After preliminary searching, 21 articles were analyzed as part of this review. Results: The following symptoms of PACS were persistently reported by a large majority (approximately ⅓ to ½) of patients: breathlessness, cough, fatigue, and brain fog. PACS neurological symptoms were more prevalent in females than in males. Reported psychiatric symptoms from prior COVID-19 infection were ADHD, depression, and insomnia. Discussion: The causes of these neurocognitive symptoms were attributed to neuroinflammation of the choroid plexus, intracerebral hemorrhagic lesions, and hypoactivity in the anterior and posterior cingulate cortex. These possible pathways were confirmed with magnetic resonance imaging (MRI) findings, computed tomography (CT) scans, and positron emission tomography (PET) scans. Conclusion: This paper will add to the evidence regarding the association between COVID-19 and the development of neurocognitive PACS. It is hoped that future research will build on a clearer understanding of the etiology of neurocognitive issues associated with viral infection.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".