Laboratory, clinical, neurological and neuropsychological features of the course of post-COVID syndrome in patients with cerebrovascular disease
Bibliographic record
Abstract
A review of the literature reveals a wide range of terms for conditions after coronavirus disease (COVID-19): post-COVID syndrome, post-acute COVID syndrome, chronic COVID-19, long-term complications of COVID-19, long COVID-19, and post-acute sequelae of severe acute respiratory syndrome coronavirus 2 infection. All these terms and others indicate that after COVID-19, a person does not return to his/her usual state of health. Many scientists are researching and looking for the causes of these symptoms, why and when they occur, and how to diagnose and treat them. Therefore, the aim of the study was to improve the diagnosis of post-COVID syndrome in patients with cerebrovascular disease (CVD) by studying clinical, neurological, laboratory and neuropsychological markers. Materials and methods. The study uses psychometric methods — Beck Anxiety Inventory, Hamilton Depression Rating Scale, Fatigue Assessment Scale; neuropsychological — Montreal Cognitive Assessment; clinical — neurological status; laboratory — hemoglobin, C-reactive protein, fibrinogen, albumin, ferritin, lactate dehydrogenase. All patients were divided into four groups: the first group included 20 people with post-COVID syndrome and CVD, the second — 15 individuals with post-COVID syndrome without CVD, the third — 15 patients without post-COVID syndrome with CVD, and the fourth — 15 people without post-COVID syndrome and without CVD. Results. In the group of patients with post-COVID syndrome with cerebrovascular disease (n1 = 20), the average level of hemoglobin (M = 115.15 ± 4.93) and albumin (M = 32.15 ± 1.53) was below the normal range; the content of fibrinogen (M = 6.04 ± 0.82), C-reactive protein (M = 5.50 ± 0.68) was above normal. Data of the Hamilton Depression Rating Scale indicate that patients with post-COVID syndrome and cerebrovascular disease Data of the Hamilton Depression Rating Scale indicate that patients with post-COVID syndrome and cerebrovascular disease (n1 = 20) had a mild depression (M = 6.75 ± 3.90; M = 8.60 ± ± 3.06). Correlation analysis revealed a direct relationship between cognitive functions and hemoglobin (r = 0.455, p ≤ 0.01), albumin (r = 0.571, p ≤ 0.01) and an inverse relationship between cognitive functions and fibrinogen (r = –0.605, p ≤ 0.01), C-reactive protein (r = –0.547, p ≤ 0.01), ferritin (r = 0.408, p ≤ 0.01). There was an inverse correlation between anxiety and hemoglobin (r = –0.619, p ≤ 0.01) and albumin (r = –0.567, p ≤ 0.01) and a direct relationship between anxiety and fibrinogen (r = 0.550, p ≤ 0.01) and C-reactive protein (r = 0.537, p ≤ 0.01). The depression scale negatively correlates with the level of hemoglobin (r = –0.597, p ≤ 0.01), albumin (r = –0.543, p ≤ 0.01) and directly with the content of fibrinogen (r = 0.433, p ≤ 0.01), C-reactive protein (r = 0.383, p ≤ 0.01) and lactate dehydrogenase (r = 0.276, p ≤ 0.05). The indicators of fibrinogen, C-reactive protein, and ferritin were the highest in the group of patients with post-COVID syndrome and cerebrovascular disease. According to the obtained data, there are statistically significant differences between four groups in cognitive functions (χ2 = 36.419, p ≤ 0.01), fatigue (χ2 = 37.251, p ≤ 0.01), anxiety (χ2 = 37.981, p ≤ 0.01) and depression (χ2 = 37.171, p ≤ 0.01). The highest rate of fatigue, anxiety, and depression was found in patients with post-COVID syndrome and cerebrovascular disease.
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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".