Post-COVID-19 Cognitive Dysfunction: Analyzing the Role of Age, Lifestyle, and Neurological Impairments A Multi-Centric Case-Control Study
Bibliographic record
Abstract
Introduction The effects of COVID-19 extend beyond acute illness, with many survivors experiencing lingering symptoms. This study investigates the frequency and contributing factors of cognitive impairment and other neurological symptoms in COVID-19 survivors four weeks after diagnosis, compared to healthy controls during the pandemic's fourth wave. Methods A case-control study was conducted involving 176 COVID-19 survivors, diagnosed four weeks prior, and 92 healthy controls from Algeria, Egypt, and Libya. Data were collected through interviews using a structured, validated questionnaire administered by a trained physician. Results Post-COVID-19 survivors exhibited significant cognitive deficits, chronic fatigue, and sensory impairments (including loss of appetite, taste, smell, and hearing). Cognitive impairment (Montreal Cognitive Assessment [MoCA] score <26) was observed in 57 participants (32.3%), with those affected being older (44.6 ± 16.9 years, P < 0.001) and consuming more junk food (8.6 ± 3.3 servings, P = 0.04). Cognitive disorders were more prevalent among females (83.3%), smokers (57.9%), highly educated individuals (76.5%), and married participants (63.7%). Discussion The study reveals a substantial burden of cognitive and sensory impairments in post-COVID-19 patients, supporting global observations and emphasizing the need for early screening and lifestyle interventions. The reliance on self-reported data and a case-control design limits causal inference. Conclusion Post-COVID-19 survivors showed significant cognitive deficits, fatigue, and sensory impairments. Cognitive impairment was present in 32.3%, with higher prevalence in females, smokers, highly educated individuals, and married participants.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".