Fatigue and cognitive impairment in post-COVID syndrome: possible treatment approaches
Bibliographic record
Abstract
Post-COVID syndrome can develop in all patients who have had COVID-19, regardless of the disease severity. Clinical manifestations postCOVID syndrome vary greatly, but the most common symptoms include fatigue, anxiety and depression disorders (ADDs), and cognitive impairment (CI). Objective: to evaluate the efficacy and safety of Cholytilin (choline alfoscerate) and the combined drug MexiB 6 in patients with post-COVID syndrome and fatigue, ADDs, and CI. Patients and methods. The study included 100 patients aged 22 to 71 years who have had COVID-19 5.4 months ago. Inclusion criterion: cognitive complaints, fatigue, and emotional disturbances. The evaluation included neurological exam, Montreal Cognitive Assessment Scale (MoCA), Frontal Assessment Battery (FAB), 10-words list task, Multidimensional Fatigue Inventory (MFI-20), Hospital Anxiety and Depression Scale (HADS). Study participants were divided into two groups. Patients who had ADDs (anxiety/depression level according to HADS ≥8 points; n=50) were prescribed with MexiB 6 (1 tablet three times per day). Patients with CI (mean MoCA score ≤25 points; n=50) were prescribed with Cholytilin (2 capsules (800 mg) in the morning and 1 capsule (400 mg) at lunchtime). The follow-up period was 60 days. Results and discussion. According to MoCA scores, a decrease in cognition was observed in 58% of participants, while 28% did not notice CI earlier. ADD fere present in 51%, and fatigue — in 100% of patients. We observed a significant reduction in fatigue severity (from 62.42±7.18 to 52.32±16.36 points; p<0.05) in patients prescribed with MexiB 6. The majority of patients noted a significant increase in physical activity, decreased fatigue, improvement of attention and physical well-being, and increased workplace efficiency. We also found a significant decrease in ADDs severity: ADDs either regressed completely (in 42% of participants) or became subclinical (in 48%; р<0.001). CI severity also reduced according to mean МоСА (from 26.60±1.31 to 27.28±1.39 points; p<0.05) and FAB (from 16.98±1.06 to 17.20±0.90 points; p<0.05) scores. In a subgroup of patients with mild CI treated with Cholytilin mean МоСА (from 23.50±0.99 to 26.36±1.34; р<0.001) and FAB (from 16.02±0.91 to 16.96±0.99; р<0.001) scores significantly increased. Complete regression of CI was observed in 74% of participants (р<0.001). We also found a decrease in ADDs (р<0.001) and fatigue (mean MFI-20 scores decreased from 42.28±10.73 to 35.60±8.10; р<0.001) severity in all study participants. Conclusion. Patients who have had COVID-19, regardless of the disease severity, have a high prevalence of fatigue, ADDs and CI, and MexiB 6 and Cholytilin have a potential in their treatment.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".