Cognitive performance and narrative discourse after SARS-CoV-2 infection
Bibliographic record
Abstract
ABSTRACT Purpose: to evaluate cognitive performance and narrative discourse, as well as possible associations in individuals affected by COVID-19. Methods: a cross-sectional exploratory research involving individuals infected by COVID-19 and hospitalized in the State of Sergipe. Participants underwent anamnesis, the Mini-Mental State Examination, Neupsilin and Montreal Toulouse Language Assessment Battery Collection (MTL/Brazil). The statistical tests employed were the Shapiro-Wilk test to assess the normality of data distribution, the nonparametric Mann-Whitney test for comparing two independent samples, and Spearman's correlation to evaluate the monotonic relationship between variables. The significance level was set at P < 0.05 Results: thirty-two individuals participated in the anamnesis (75% males, 25% females). A significant correlation was found between the working memory and discourse skills (P < 0.01). Discourse analysis using the Mann-Whitney test revealed significant differences in the total number of scenes (P = 0.005; d = 0.5) and the total number of Information Units (P = 0.017; d = 0.3). These findings suggest that COVID-19 has a substantial impact on speech, affecting verbal fluency, auditory span, digit sequencing, and working memory, thereby influencing memory storage and retrieval processes. Conclusion: the impact of the pandemic in this area covers a wide range of cognitive and discursive skills.
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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 | 0.005 |
| 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 | 0.001 | 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".