Bibliographic record
Abstract
Introduction: The Coronavirus disease 2019 (COVID-19) spread, causing a worldwide pandemic and affecting multiple organs and systems. The possible long-term sequelae of COVID-19 have become an increasing concern. Currently, little information exists about prolonged COVID-19 affects related to cognitive functions. Objective: The study aimed to investigate the cognitive functions of patients who recovered from COVID-19 at least three months after the diagnosis. Methods: A cross-sectional study was conducted to investigate cognitive functions among 150 employees of Buddhasothorn Hospital, Chachoengsao, Thailand. Of these, 75 employees had a history of COVID-19 at least three months after the diagnosis. Demographic characteristics were recorded and screened for depression, anxiety and insomnia. They were tested for their cognitive functions using the Montreal Cognitive Assessment (MoCA) and compared with 75 employees without a history of COVID-19. Results: All postCOVID-19 cases presented mild COVID-19 symptoms. The results showed that 96% of COVID-19 in both groups, cases and the healthy group, had normal cognitive functions using the MoCA that did not significantly differ. However, the depression score in the postCOVID-19 cases was significantly higher than that of the participants without a history of COVID-19 (1.09 ± 1.36 and 0.61 ± 1.09, respectively (p = 0.018). Regression analysis between the postCOVID-19 cases and depression using multivariate analysis showed that the postCOVID-19 cases were associated with depression scale (β coefficient=0.470; 95%CI: 0.073, 0.867, respectively), after adjusting for age, sex, educational level and underlying diseases. Conclusion: The cognitive functions of employees having a history of COVID-19 and without infection did not differ.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".