Neuropsychological deficits in patients with cognitive complaints after COVID‐19
Bibliographic record
Abstract
BACKGROUND: While much of the scientific focus thus far has been on cognitive sequelae in patients with severe COVID-19, subjective cognitive complaints are being reported across the spectrum of disease severity, with recent studies beginning to corroborate patients' perceived deficits. In response to this, the aims of this study were to (1) explore the frequency of impaired performance across cognitive domains in post-COVID patients with subjective complaints and (2) uncover whether impairment existed within a single domain or across multiple. METHODS: Sixty-three patients with subjective cognitive complaints post-COVID were assessed with a comprehensive protocol consisting of various neuropsychological tests and mood measures. Cognitive test performance was transformed into T scores and classified based on recommended guidelines. After performing a principal component analysis to define cognitive domain factors, distributions of test scores within and across domains were analyzed. RESULTS: Results revealed pervasive impact on attention abilities, both as the singularly affected domain (19% of single-domain impairment) as well as coupled with decreased performance in executive functions, learning, and long-term memory. These salient attentional and associated executive deficits were largely unrelated to clinical factors such as hospitalization, disease duration, biomarkers, or affective measures. DISCUSSION: These findings stress the importance of comprehensive evaluation and intervention to address cognitive sequelae in post-COVID patients of varying disease courses, not just those who were hospitalized or experienced severe symptoms. Future studies should investigate to what extent these cognitive abilities are recuperated over time as well as employ neuroimaging techniques to uncover underlying mechanisms of neural damage.
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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 | 0.000 | 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) | 0.001 | 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".