Neuropsychological disorders after COVID-19. Urgent need for research and clinical practice
Bibliographic record
Abstract
Purpose: Numerous studies suggest that infection with coronavirus SARS-CoV-2, which causes acute respiratory distress syndrome and COVID-19 illness, can lead to changes in the central nervous system (CNS). Consequently, some individuals with SARS-CoV-2 infection may also present the symptoms of neuropsychological disorders. The goals of this literature review is the synthesis of various perspectives and up-to-date scientific knowledge as well as the formulation of initial recommendations for clinical practice. Views: According to current state of knowledge, numerous SARS-CoV-2 infection-specific and nonspecific risk factors exist for brain damage, which might lead to neuropsychological impairments in individuals who have recovered from COVID-19. The emerging evidence suggests significant behavioral and cognitive deficits in COVID-19 survivors, which are present in the early phase after recovery and persist for several months. Neuropsychological disturbances can potentially include a wide spectrum of disorders, yet deficits of attention, memory, executive functions, language and visuospatial orientation are among most commonly identified. The relationship between cognitive impairment, emotional disturbances and severity of COVID-19 symptoms needs to be submitted to further research. Conclusions: The scientific knowledge resulting from neuropsychological empirical studies during the COVID-19 pandemic allows for a postulate of an urgent evidence-based systematic neuropsychological research to be conducted among COVID-19 survivors. More than anything, the recovered individuals must be provided with adequate neuropsychological help in the form of neuropsychological diagnosis, monitoring and rehabilitation.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".