Cognitive Impairment and Risk Factors in Post-COVID-19 Hospitalized Patients
Bibliographic record
Abstract
Introduction: Numerous reports regarding cognitive deficits after the coronavirus disease 2019 (COVID-19), described as "brain fog," have been published. However, the clinical presentations and risk factors of post-COVID-19 cognitive impairment are controversial. This study aimed to assess (a) the prevalence of cognitive impairment after COVID-19 hospitalization, (b) characteristics of the cognitive deficits, (c) risk factors of post-COVID-19 cognitive impairment, and (d) comparison of cognitive function between post-COVID-19 patients and healthy people. Methods: The study comprised 34 SARS-CoV-2-infected patients, admitted to the Neurological Institute of Thailand during the peak of COVID-19 pandemic in 2021-2022. These patients came for neuropsychological and clinical evaluations at 2-week follow-up visit. The cognitive impairment and characteristics were measured by TMSE and MoCA. Clinical risk factors and post-COVID-19 cognitive impairment were assessed. The comparison of cognitive function in post-acute COVID-19 patients and 22 healthy controls was also performed. Results: The prevalence of post-COVID-19 cognitive impairment defined by a total MoCA score below 25 points was 61.76%. Years of education were the only predictive factors related to cognitive impairment. Our multivariate analysis revealed no statistical difference in cognitive outcomes between post-acute COVID-19 patients and healthy controls. Conclusion: This study showed a moderate prevalence of cognitive dysfunction after COVID-19 hospitalization similar to previous reports. However, there was no significant difference in cognitive measurements between these patients and healthy people. Whether SARS-CoV-2 infection causes cognitive dysfunction is a myth or fact that still has a long way to prove via further longitudinal study.
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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 | 0.000 |
| 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) | 0.001 | 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".