Cognitive effects of COVID-19
Bibliographic record
Abstract
Objective: Neurological involvement can be seen in Covid-19 and cognitive functions can be affected negatively. There is not enough study and data about cognitive functions of patients followed with Covid-19 diagnosis. For this reason, the current study aims to examine the cognitive functions of patients diagnosed with Covid-19. Material and Methods: Patients, who were hospitalized and followed-up with the diagnosis of Covid-19, were selected as consecutive. For evaluating cognitive functions, Montreal Cognitive Assessment (MoCA) test was performed to under 65-year-old participants, Standardized Mini- Mental State Exam (MMSE) test was performed to 65-year-old and older participants and Hospital Anxiety and Depression Scale (HADS) was performed to all participants. Results: The study has consisted of 32 patients and 35 volunteers. The mean of MMSE scores was found to be lower in the patient group than the control group (p>0.05). The mean of MoCA scores was found to be lower in the patient group than the control group, and it was statistically significant (p<0.05). Also, in the sub-group analyzes that assessed the attention of the MoCA test group, the patient group scores were lower than the control group and it was statistically significant (p<0.05). Hospital anxiety scale scores of the patient group were found to be significantly higher than the control group (p<0.05). Conclusion: The result of this study is showed the presence of cognitive impairment and increased anxiety in patients with Covid-19 who had no previous cognitive deficit. Therefore, it is important to evaluate the patients with Covid-19 in terms of mood and cognition in detail.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".