Cognitive disorders in people who have had COVID-19
Bibliographic record
Abstract
Relevance. COVID-19 infection has a significant damaging effect on the cognitive functions of people who have had this infection, and also causes changes in their emotional state. The study of the causes and features of clinical manifestations of cognitive and emotional disorders in such individuals is necessary to develop effective diagnostic methods and develop a rehabilitation program to improve their social functioning. The aim of the study was to develop a rehabilitation program for people with cognitive and emotional disorders due to COVID-19 infection by analyzing the severity and structure of their cognitive impairment and studying the associated emotional symptoms. Materials and Methods. The study was based on the results of a survey of people of different ages who had cognitive and emotional disorders due to COVID-19. The study was conducted using theoretical (literature review) and empirical methods (psychodiagnostic methods - Montreal Cognitive Assessment (MoCA), Schulte Tables, 10 Words by A. R. Luria, Hospital Anxiety and Depression Scale (HADS)). Results and Discussion The severity and structure of cognitive and emotional disorders differ by age. Older people have more severe cognitive impairment and a higher proportion of anxiety disorders, while younger people have less cognitive deficits and a prevalence of depressive symptoms. The most common disorders in both age groups are memory and sensorimotor reaction time disorders. Conclusions. Rehabilitation programs for these individuals should be aimed at reducing cognitive deficits, concomitant emotional disorders, and improving their social functioning. Programs should be personalized, taking into account the specifics of the disorders in each case.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".