Assessment of Cognitive and Mood Changes in Older Survivors of COVID-19
Bibliographic record
Abstract
ABSTRACT: The long-term effects of coronavirus disease 2019 (COVID-19) infection are not fully known. In this study, we aimed to evaluate cognitive function and mood changes with 1-year follow-up in the elderly after COVID-19 disease. Ninety COVID-19 survivors and 90 healthy controls were included in the study between April 2022 and 2023. The patients were evaluated at the 1st, 6th, and 12th months for cognition, depression, and sleep quality. Cognitive function is assessed by the Montreal Cognitive Assessment (MoCA), sleep quality by the Pittsburgh Sleep Quality Index, and depression by the Yesavage Geriatric Depression Scale. COVID-19 survivors secured lower scores in certain domains of the MoCA in comparison with the controls at the first and sixth months. However, at the 12th month, no difference was observed in total MoCA ( p = 0.100), Yesavage Geriatric Depression Scale ( p = 0.503), and Pittsburgh Sleep Quality Index ( p = 0.907) between survivors and controls. Older patients who recovered from COVID-19 have lower cognitive function compared with controls up to 12 months. However, cognitive function scores were similar at the end of the first year except for memory scores.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".