Assessment of cognitive and psychiatric disturbances in people with post-COVID-19 condition: a cross-sectional observational study
Bibliographic record
Abstract
OBJECTIVE: Cognitive and psychiatric symptoms have been increasingly reported after severe acute respiratory syndrome coronavirus 2 infection, developing soon after infection and possibly persisting for several months. We aimed to study this syndrome and start implementing strategies for its assessment. METHODS: Consecutive patients, referred by the infectious disease specialist because of cognitive complaints after COVID-19, were neurologically evaluated. Neurological evaluation included a cognitive screening test (Montreal Cognitive Assessment, MoCA). Moreover, patients were invited to fill out a general symptom questionnaire and a self-administered multidimensional assessment of psychiatric symptoms, followed by a full psychiatric assessment if scores were above validated cutoffs. RESULTS: Of 144 referred patients, 101 (mean age 55.2±13.1, 63.4% females) completed the cognitive screening and the self-administered psychiatric questionnaire. Acute infection severity was low for most patients and the most common persisting symptoms were fatigue (92%), sleep problems (69.5%), and headache (52.4%). MoCA outlined cognitive deficits in ≥1 cognitive domain in 34% of patients, mainly in memory and attention. About 60% of patients presented depressive, anxiety, or stress-related symptoms. Psychiatric scale scores significantly correlated with overall symptom burden and MoCA score. No significant correlation was found between MoCA scores and overall symptom burden. CONCLUSION: We hypothesize that persistent cognitive complaints after COVID-19 might reflect a concomitant or reactive psychopathological condition, possibly coupled with an infection-related impact on cognitive functions. The application of a combined neurological and psychiatric assessment seems crucial to appraise the nature of post-COVID-19 condition.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".