LONG-COVID COGNITIVE IMPAIRMENT: COGNITIVE ASSESSMENT AND APOLIPOPROTEIN E (APOE) GENOTYPING CORRELATION IN A BRAZILIAN COHORT
Bibliographic record
Abstract
Background: COVID-19 neurological manifestations were demonstrated during the pandemic, including cognitive impairment. Objectives: To determine the prevalence of cognitive and behavioral complaints (such as dementia, MCI or SCD) in a outpatient sample with recent SARS-COV2 infection. Specific: Evaluate the association of cognitive impairment with the presence of the polymorphism found in the APOE gene and with respiratory disease. Methodology: Observational, longitudinal, prospective clinical study. Inclusion criteria: patients with confirmed Covid-19. Patients are evaluated in an outpatient clinic. They are evaluated through a standardized attendance record, with somatic and cognitive neurological assessment. Cognitive assessment involves the application of cognitive (ACER, MMSE and CDR), functional (Pfeffer) and psychiatric (GDS or Beck) screening instruments, in addition to subsequent extensive neuropsychological assessment. In addition, APOE polymorphism is analysed. Preliminary. Results: To date, 191 patients and 11 controls were evaluated. The average age is 46.5 years, with 65.4% female, 79.16% with 8 or more years of schooling, in addition to 57.5% of the sample with cognitive complaints. Conclusions: The results so far in our study demonstrate that cognitive complaints are frequent in patients even in the chronic phase of the disease.
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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 | 0.000 | 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".