Cognitive Impairment in Long COVID Patients Presenting with Psychiatric Sequelae: A Cross-sectional Study
Bibliographic record
Abstract
Introduction: Coronavirus Disease-2019 (COVID-19) affects mental health, causing various psychiatric symptoms, including cognitive impairment, which may persist for a long time. To develop effective strategies for combating this global health burden, it is necessary to ascertain whether COVID-19 itself causes cognitive decline or whether other factors also play any role. Aim: To determine the prevalence of cognitive impairment in long COVID patients who present with post-COVID-19 psychiatric sequelae, and to investigate its association with socio-demographic factors, depression, anxiety, and stress. Materials and Methods: A cross-sectional study was conducted from July 2022 to June 2023 at a ‘Post-COVID Mental Health Clinic’ in a tertiary care medical college in Kolkata, India. A total of 204 subjects were selected through simple random sampling, aged between 18 and 65 years, of both sexes, who had recovered from COVID-19 more than three months but less than six months prior, and who presented with post-COVID-19 psychiatric sequelae, excluding those with a history of psychiatric disease before contracting COVID-19. The dependent variable, cognition, was measured using the Montreal Cognitive Assessment (MoCA) score, while independent variables included socio-demographic factors, depression, anxiety, and stress, measured by the Depression Anxiety Stress Scale -21 (DASS-21) scores. The Chisquare test was used to find the association between cognition and socio-demographic variables and Pearson’s correlation test was applied to measure the association of cognition with depression, anxiety, and stress scores. Results: The prevalence of cognitive impairment was found to be 86.8%. Chi-square tests of association showed no significant association with socio-demographic factors. However, there was a significant correlation between the severity of depression (r-value=-0.337, p-value<0.001), anxiety (r-value=-0.275, p-value<0.001), and stress (r-value=-0.277, p-value<0.001) with cognitive impairment. When controlling for anxiety and stress, only depression showed a significant correlation (r-value=- 0.221, p-value=0.002). Simple linear regression indicated that the severity of depression significantly predicted the severity of cognitive impairment {R2 =0.114, F(1, 202)=25.88, p-value<0.001}. Conclusion: Cognitive impairment was found to be unrelated to socio-demographic factors, post-COVID-19 anxiety, or stress, except for post-COVID-19 depression, which was identified as a significant predictor of cognitive dysfunction in some patients. This suggests that COVID-19 infection itself may be the most important factor contributing to post-COVID-19 cognitive impairment in patients with post-COVID-19 psychiatric sequelae.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".