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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".