Incidence and risk factors of cognitive impairment in COVID-19 survivors within the first six months and its association with functional outcome
Bibliographic record
Abstract
Background & Objective: There is growing evidence of cognitive decline post-COVID-19, even in mild infection. We aim to evaluate the incidence of cognitive impairment, domains affected, its risk factors and the effect on function during the subacute period when rehabilitation is crucial. Methods: In this study, the incidence of impaired cognition was assessed in patients at 3 and 6 months post-COVID-19 infection between August 2021 and July 2022 at University Malaya Medical Center, with Montreal Cognitive Assessment (MoCA). The most common cognitive domains affected were identified with descriptive analysis. Associated sociodemographic and clinical factors were analyzed with simple and multiple logistic regression models. The post-COVID-19 Functional Scale (PCFS) was used to assess functional status. The correlation between cognition (MoCA score) and functional status (PCFS scale) was performed using Spearman correlation test. Results: We recruited 100 patients and found that 44% had impaired cognition at three months and 43% at six months. Patients with secondary education level (p =0.001, OR 13.541), oxygen therapy (p=0.039, OR 7.811), and obesity (p=0.029, OR 4.764) were associated with a higher risk of impaired cognition. The most affected MoCA domains were language, executive function, attention and memory. Lower MoCA score was correlated with higher PCFS grade (lower functional status) (p <0.001, ρ -0.729). Conclusion: Post-COVID-19 cognitive impairments were common up to 6 months of illness and affect function. Clinicians are advised to perform cognitive screening especially in higher risk patients and provide necessary interventions.
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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".