Persistent (>6 months) Neuropsychiatric symptoms in a cohort of SARS- CoV-2 PCR positive patients in Cape Town, South Africa
Bibliographic record
Abstract
Background : SARS-CoV-2 is a neurotrophic and pro-inflammatory virus, with several acute and more persistent neuropsychiatric sequelae reported. There are limited data from younger African cohorts and few acute illness biomarkers of persistent neuropsychiatric symptoms. Objectives : To determine the prevalence of neuropsychiatric symptoms in a cohort of South African SARS-CoV-2 PCR positive patients at least six months following infection/hospitalization. Second, to examine the association of neuropsychiatric outcomes with clinical illness severity, systemic inflammation, cardiovascular and renin-angiotensin system (RAS) biomarkers. Methodology: SARS-CoV-2 PCR positive patients were recruited prospectively from Cape Town, South Africa, including hospitalized patients from ancestral, beta and delta-dominant COVID-19 waves (pre-vaccine rollout); and asymptomatic/mild SARS-CoV-2 positive patients. Telephonic interviews were conducted at least six months post infection/hospitalization. Validated measures employed were: WHO Self-Report Questionnaire (SRQ-20), Generalized Anxiety Disorder Scale (GAD-7), Chalder Fatigue Scale (CFS-11) and Telephonic Montreal Cognitive Assessment (T-MoCA). The 96-protein O-link inflammation and cardiovascular panels, RAS fingerprinting, and antibody responses were measured in plasma/serum samples collected at peak severity and recovery (>3 months post infection). Results: Ninety-seven participants completed telephonic interviews. The median (IQR) age was 48 (37-59) years, and 54% were female. More than half of this South African COVID19 cohort had one or more persistent neuropsychiatric symptoms >6 months post vaccine-naïve infection. On the T-MoCA, 44% of participants showed evidence of cognitive and/or memory impairments. There were no significant associations between neuropsychiatric outcomes and illness severity, systemic inflammation, cardiovascular and renin-angiotensin-system (RAS) biomarkers. Conclusion : The high prevalence of persistent neuropsychiatric symptoms in this young African cohort supports ongoing attention to long COVID. Persistent neuropsychiatric outcomes post-COVID are not associated with systemic inflammation or altered renin angiotensin physiology. Psychosocial variables affecting individual responses to the virus may explain the lack of association found on the biological front.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".