Cognitive impairment as a sequel to SARS-CoV-2 infection
Bibliographic record
Abstract
Introduction: COVID-19 survivor patients report a complaint subjectively related to memory and attention-concentration problems when carry out their daily activities. Objective: To investigate the presence of cognitive sequelae associated with COVID-19. Method: An observational cross-sectional study was conducted. Participated 229 COVID-19 survivor patients, who were evaluated with Montreal Cognitive Assessment (MoCA) and classified into three groups: a) severe cognitive impairment (G1, <13points, n=11), b) mild cognitive impairment (G2, 13-21 points, n=34), and c) without cognitive alterations (G3, 22-30 points, n=184). Statistical analyses were performed in SPSS V25. Results: Average age and male sex: G1: 67.36±10.71, 63.6%; G2: 58.76±11.55, 61.8%; G3: 53.32±11.39, 58.7%. G1 presented statistically differences (p <0.001) in all cognitive functions with the other groups (G1, G2, G3): visuospatial/executive ability (0.09±0.30, 0.53±0.50, 0.83±0.38), identification (2.00±0.89, 2.82±0.38, 2.92±0.32), attention (2.45±0.82, 3.97±1.16, 5.02±0.97), repetition (0.18±0.40, 0.29±0.46, 1.02±0.79), fluency (0.18±0.40, 0.68±0.47, 0.93±0.24), abstraction (0.18±0.40, 0.68±0.72, 1.41±0.77), deferred remembering (0.73±1.10, 1.44±1.37, 3.13±1.36) and orientation (4.64±0.92, 5.35±0.73, 5.79±0.49); except in executive ability, identification, repetition, abstraction and deferred remembering with respect to G2 (p<0.05). Conclusions: Cognitive alterations were found in patients post- COVID, especially in attention and deferred remembering, which could be related to errors of execution in other areas. In the case of the severe cognitive impairment, age may be a related variable. Necessary integrate this variable in the rehabilitation plan for a better prognosis and quality of life of patients.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".