Cognitive impairments, depression and quality of life 1 year after COVID-19 hospital discharge
Bibliographic record
Abstract
Purpose: Following the acute stage of COVID-19, many patients have experienced persistent physical and neuropsychiatric symptoms, regardless of the severity of the illness. Key concerns like fatigue, depressive symptoms, and cognitive impairments have a detrimental effect on psychosocial functioning and quality of life. This study aims to explore cognitive impairments and depression one year after discharge from the hospital for COVID-19, along with implications for quality of life. Materials and Methods: Patients hospitalized with COVID-19 with mild-moderate disease severity were asked to participate in a 12-month follow-up evaluation. A total of 60 individuals who had recovered from COVID-19 were examined for cognitive functions, depressive symptoms, and quality of life. They were then compared to a healthy comparison group, matched for age and education, who had not been infected with COVID-19. Results: After 1 year following discharge, cognitive impairment was noted in 21.7% of COVID-19 patients, based on The Montreal Cognitive Assessment, while only 3.3% of the non-COVID-19 control group exhibited the same. Quality of life domains were largely comparable across groups. Depressive symptoms were identified as the main factor affecting quality of life domains in COVID-19 patients. Conclusion: Despite the potential for cognitive impairment to last for up to a year following hospital discharge, it does not seem to have a negative effect on the quality of life in our group of patients with mild-to-moderate disease severity. However, depressive symptoms were found to be the key indicators of quality of life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".