Personality traits as risk factors for the development of cognitive impairment and affective symptoms in patients with COVID-19
Bibliographic record
Abstract
OBJECTIVE: To identify possible associations between premorbid personality traits and cognitive impairment and affective symptoms in patients who have recovered from COVID-19. MATERIAL AND METHODS: The study included 30 people with the so-called post-COVID syndrome. The diagnosis of COVID-19 was previously confirmed by laboratory tests in each patient. The control group included 15 healthy individuals. The Hospital Anxiety and Depression Scale was used to assess depression and anxiety. Cognitive function was assessed using the Verbal Fluency Test (VF), the Montreal Cognitive Scale (MOCA), and the Wisconsin Card Sorting Test (WCST). The Munich Personality Test (MRT) and the Toronto Alexithymia Scale (TAS-26) were used to assess premorbid personality characteristics. Multiple stepwise regression analysis was used as the main statistical method to identify the relationship between premorbid personality constructs and cognitive test results and affective and anxiety symptoms. RESULTS: The presence of frustration tolerance in the personality structure reduced the number of incorrect answers (beta coefficient -0.811) in WCST and decreased the delay in responses with positive reinforcement (-0.630), and also reduced the level of depression (-0.465). Extraversion decreased the MOCA test score (-0.675) and increased the percentage of perseverative incorrect answers on the WCST test (0.573). The constructs of adherence to social norms and propensity to isolate lowered the final MOCA score (beta coefficients are -0.725 and -0.527, respectively). The esoteric tendencies construct decreased the latency of positive and negative reinforcement responses in WCST (-0.441 and -0.528, respectively). The severity of alexithymia was positively correlated with depression (beta 0.577), while neuroticism was positively correlated with anxiety (0.737). CONCLUSION: Low levels of frustration tolerance and esoteric tendencies have negative effects on cognition in COVID-19 survivors, while high levels of these constructs are protective against cognitive decline and depression.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".