Cognitive Impairment in Post-COVID-19 Patients: A Prospective Neuropsychological Evaluation Study
Bibliographic record
Abstract
Background: Cognitive impairments, often termed “brain fog,” have emerged as prevalent sequelae among individuals recovering from COVID-19. Despite increasing recognition, systematic neuropsychological evaluation data remain limited. Methods: A prospective observational study was conducted between February 2023 and December 2024 in a tertiary care neurorehabilitation center. One hundred twenty post-COVID-19 patients (mean age 44.6 ± 11.3 years; 58.3% male) presenting with cognitive complaints at least 3 months after recovery were recruited. Patients underwent evaluation using the Montreal Cognitive Assessment (MoCA), Trail Making Test A and B (TMT-A, TMT-B), and Digit Span Test. A control group of 60 age- and education-matched individuals without prior COVID-19 served as comparison. Statistical analysis included independent t-tests, chi-square tests, and multivariate logistic regression ( P < 0.05). Results: Cognitive impairment (MoCA score <26) was observed in 66.7% of post-COVID-19 participants compared to 15.0% of controls ( P < 0.001). Post-COVID patients showed significantly slower TMT-A (43.7 ± 12.9 vs 31.2 ± 9.6 sec; P < 0.001) and TMT-B times (95.8 ± 22.4 vs 76.5 ± 17.8 sec; P < 0.001). Working memory (Digit Span backward) was also impaired (mean score: 4.2 ± 0.9 vs 5.3 ± 0.8; P < 0.001). Severity of initial infection (hospitalized vs non-hospitalized) was associated with increased odds of impairment (OR: 2.87; 95% CI: 1.22–6.74; P = 0.015). Conclusion: Cognitive deficits are common in post-COVID-19 patients, particularly affecting attention, processing speed, and working memory. Structured cognitive screening and rehabilitation may be essential components of long-term COVID-19 care.
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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 | 0.001 |
| 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 | 0.001 |
| 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".