Multidomain cognitive impairment in non-hospitalized patients with the post-COVID-19 syndrome: results from a prospective monocentric cohort
Bibliographic record
Abstract
BACKGROUND: A fraction of patients with asymptomatic to mild/moderate acute COVID-19 disease report cognitive deficits as part of the post-COVID-19 syndrome. This study aimed to assess the neuropsychological profile of these patients. METHODS: Assessment at baseline (three months or more following acute COVID-19) of a monocentric prospective cohort of patients with post-COVID-19 syndrome. Multidomain neuropsychological tests were performed, and questionnaires on depression, anxiety, fatigue, sleep, and general health status were administered. RESULTS: Of the 58 patients screened, six were excluded due to possible alternative causes of cognitive impairment (major depression, neurodegenerative disease). Of the remaining 52 individuals, only one had a below-threshold screening result on Mini-Mental State Examination, and 13 scored below the cut-off on Montreal Cognitive Assessment. Extended neuropsychological testing revealed a neurocognitive disorder (NCD) in 31 (59.6%) participants with minor NCD in the majority of cases (n = 26). In patients with NCD, the cognitive domains learning/memory and executive functions were impaired in 60.7%, complex attention in 51.6%, language in 35.5%, and perceptual-motor function in 29.0%. Cognitive profiles were associated with daytime sleepiness but not with depression, anxiety, sleep quality, total general health status, or fatigue. CONCLUSION: Neurocognitive impairment can be confirmed in around 60% of individuals with self-reported deficits as part of post-COVID-19 syndrome following a mild acute COVID-19 disease course. Notably, screening tests cannot reliably detect this dysfunction. Standard psychiatric assessments showed no association with cognitive profiles. Longitudinal studies are needed to further evaluate the course of neurocognitive deficits and clarify pathophysiology.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".