Prevalence of Cognitive Impairment and Its Risk Factors in Patients with COVID-19: A Follow-up Study
Bibliographic record
Abstract
Background: Cognitive impairment is a recognized consequence of COVID-19, persisting long after recovery. This study investigates the prevalence and risk factors associated with cognitive impairment in post-COVID-19 patients. Objectives: The present study aimed to assess cognitive function at three months and one year post-hospitalization using the blind montreal cognitive assessment (blind MoCA) and analyze its correlation with demographic, clinical, and laboratory variables. Methods: A longitudinal study was conducted on 260 hospitalized COVID-19 patients. Cognitive function was assessed using the blind MoCA, and statistical analyses, including t-tests, ANOVA, and correlation analysis, were performed to identify significant predictors of cognitive decline. Results: Cognitive scores significantly declined from three months (19.37 ± 1.54) to one year (18.85 ± 1.67) (P = 0.0001). Older age, elevated C-reactive protein (CRP) and creatinine levels, psychiatric history, hypertension, and severe pulmonary involvement were associated with worse cognitive outcomes. Conclusions: Post-COVID-19 cognitive impairment worsens over time and is influenced by inflammation, oxygenation, and comorbidities. Early intervention strategies, including cognitive rehabilitation and monitoring of high-risk individuals, are essential to mitigate long-term cognitive impairment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 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".