Cognitive after-effects and associated correlates among post-illness COVID-19 survivors: a cross-sectional study, Egypt
Bibliographic record
Abstract
Abstract Background COVID-19's after-effects among survivors are of increased concern. The cognitive aftermath of COVID-19 virus infection was underrated. This study aimed to identify and compare the cognitive impairment (CI) and its correlates among COVID-19 survivors and control subjects. A total of 85 adults who survived COVID-19 virus infection and an equal number of control subjects (matched for age, sex, education, and socioeconomic level) were included in this study. They were recruited from Zagazig University Hospitals, Sharkia Province, Egypt. All subjects were interviewed utilizing a semistructured demographic and clinical checklist, the Montreal Cognitive Assessment (MoCA) test, and the Hospital Anxiety Depression Scale (HADS). Results More than half of COVID-19 survivors experienced CI (compared to only 8% of control subjects). Individuals who survived COVID-19 virus infection were more likely to have impairments in visuo-executive functions (OR: 0.3, 95% CI 0.2–0.5), attention (OR: 0.4, 95% CI 0.3–0.7), language (OR: 0.2, 95% CI 0.1–0.5), delayed recall (OR: 0.5, 95% CI 0.4–0.6), and total MoCA Scores (OR: 0.1, 95% CI 0.04–0.2). Among COVID-19 survivors, those who experienced CI were likely to be older (OR: 1.1, 95% CI 1.03–1.2), and of low-to-moderate education (OR: 4.9, 95% CI 1.6–15.1). Conclusions CI was prevalent among COVID-19 survivors. The visuo-executive functions, attention, language, and delayed recall were the most affected domains. Older age and lower educational level predicted CI in COVID-19 survivors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".