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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".