Long COVID and Low Education: Neuropsychological Findings from the Brazilian NeuroCovid Cohort
Bibliographic record
Abstract
BACKGROUND: Long COVID is a chronic condition that persists for at least three months after SARS-CoV-2 infection. It is characterized by a wide range of symptoms, including neurological manifestations. This study aimed to investigate the influence of long COVID on neuropsychological performance in adults with different educational levels, categorized into high and low education groups. METHOD: We included community-dwelling individuals from Porto Alegre, Brazil. Participants were divided into four groups based on education and long COVID status: Control-Low Education (CLE), Control-High Education (CHE), Long COVID-Low Education (LCOVID_LE), and Long COVID-High Education (LCOVID_HE). High education was defined as having more than 11 years of schooling. Neuropsychological assessments included the Mini-Mental State Examination (MMSE), the Trail Making Test (TMT-B), and the Wechsler Memory Scale (WMS-R). Group differences in neuropsychological performance were analyzed using Analysis of Variance (ANOVA). Post hoc pairwise comparisons were conducted for significant main effects.a A Kruskal Wallis analysis was used for non-parametric tests. Statistical analyses were performed using R software, with a significance threshold set at p <0.05p<0.05. RESULT: A total of 122 individuals were included, with a mean age of 59.6 years (± 14.9), of whom 73.8% were female. Demographic details are shown in Figure 1. Participants in the CLE group scored significantly lower on the MMSE than the CHE group (p = 0.007). Similarly, the LCOVID_LE group scored significantly lower than the CHE group (p = 0.009). A significant result was found between MMSE and education (p = 0.003) in Kruskal Wallis analysis. However, no significant effects of long COVID were observed in other neuropsychological domains assessed. CONCLUSION: Our study highlighted the significant interplay between educational attainment, which contributes to cognitive reserve, and the neurological manifestations of long COVID. Higher educational attainment may confer resilience against cognitive decline associated with long COVID. While long COVID was linked to lower global cognitive scores, no specific cognitive domain appeared particularly vulnerable. These findings underscore the importance of prioritizing support for vulnerable populations with low education to promote brain health.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| 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".