Specificity of cognitive dysfunction in the context of post-COVID syndrome in patients with acute cerebrovascular lesions
Bibliographic record
Abstract
The article analyzes the specificity of cognitive dysfunction in patients with acute cerebrovascular lesions in the context of post-COVID syndrome and the development of methods for assessing their impact on quality of life. The scientific studies that analyze the dynamics of cognitive functions depending on the severity of COVID-19, methods of ventilation therapy and the age of patients are considered. It has also been found that acute and chronic stress related to COVID-19 can affect the activation of inflammatory processes and worsen the symptoms of depression. The study was carried out using a number of neuropsychological tests, including the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), 10-word test, Schulte tables, paired associates learning test, which allowed for a comprehensive assessment of cognitive disorders and their impact on patients’ quality of life. Individuals with cerebrovascular disease who recovered from COVID-19 were found to have moderate to severe cognitive impairment compared to controls. Attention and executive functions were particularly often impaired, while delayed and recognition memory were less affected. Analysis using the MMSE and MoCA confirmed the higher sensitivity of the MoCA in detecting minor changes in cognitive functioning, which helps in the diagnosis of cognitive impairment in patients with cerebrovascular disease after COVID-19. There was a need for long-term monitoring and timely treatment of cognitive impairment, as most patients continued to exhibit cognitive dysfunction within six months of recovery. The results of the study indicate the need for further research to assess the long-term impact of SARS-CoV-2 on cognitive functions and the development of effective treatment strategies using neuropsychological support and cognitive training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".