Cognitive performance in long COVID and its association with sociodemographic factors, self-perceived health, and vaccination
Bibliographic record
Abstract
Cognitive complaints are common among patients affected by COVID-19, and it is known that some type of persistent cognitive impairment is highly prevalent in the presence of prior vulnerability. Therefore, this study investigated the association between cognitive performance and sociodemographic factors, self-perceived health, vaccine doses, and number of infections in patients with long COVID. A cross-sectional study was conducted in a specialized outpatient clinic. A questionnaire was applied to collect age, gender, education level, infections, COVID-19 vaccine doses, and self-perceived health. The Montreal Cognitive Assessment (MoCA) was used for cognitive screening. Spearman’s correlation was tested between variables. Thirty-three individuals participated: 25 women (75.7%) and 8 men (24.4%). The mean age was 51.2 years (SD=10.84). Most participants had completed secondary (18.2%) or higher education (48.5%). A statistically significant correlation was found between MoCA scores and education (r=0.640, p<0.01), and self-perceived health (r=0.433, p<0.007). Higher education and greater subjective well-being are associated with better cognitive performance, possibly due to cognitive reserve, which delays onset of neurodegenerative disease symptoms. It has been suggested that individuals vaccinated against COVID-19 have better cognitive performance than unvaccinated ones. On the other hand, negative self-perception of health and repeated infections have been associated with persistent cognitive impairments. In the present study, no associations were observed between cognition, vaccination, and reinfections, which may be due to the small sample size. Further studies are needed to clarify these relationships.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".