Incidence of cognitive, depressive and anxiety disorders in COVID‐19 patients
Bibliographic record
Abstract
Abstract Background The COVID‐19 pandemic has become a serious global problem that has affected almost all aspects of modern society, especially public health. Among the significant health problems one can single out ‐ an increase in the number of people with cognitive, depressive and anxiety disorders in COVID‐19 patients. These disoders may be due to the severity of the patient’s condition during the period of COVID‐19 disease, the presence of significant events after suffering COVID‐19 or premorbid cognitive (vascular pathology or Alzheimer’s disease) and emotional status. Method We examined 60 patients after COVID‐19, 33 (55%) were men, at 7.34 ± 0.14 months follow‐up after hospital treatment. Mean age ‐ 59.93±1.32. Participants were completed the Montreal Cognitive Assessment (MoCA) and The Frontal Assessment Battery (FAB). Depression and anxiety were identified using the Hospital Anxiety and Depression Scale (HADS). Result According to the measured MoCA, 30 (50%) of the examined patients had mild cognitive impairments, no severe cognitive impairments were detected. According to the FAB scale, 25 (41.7%) patients had moderate frontal dysfunction, no severe frontal dysfunction were detected. According to the HADS scale: anxiety was detected in 10 (16.6%) patients; depression — in 3 (5%) patients. The study found evidence that cognitive impairment was more common in patients with a low level of education, atherosclerosis of the brachiocephalic arteries, and chronic heart failure (p<0.05). Cognitive impairment and anxiety disorders were significantly more frequently recorded in older patients (p<0.05). Conclusion COVID‐19 survivors showed a considerable prevalence of cognitive impairment. Considering the alarming impact of COVID‐19 infection on mental health, we recommend to assess cognitive and emotional status of COVID‐19 survivors for early diagnosis and treatment of cognitive and emotional disorders.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".