Cognitive and Psychological Sequelae of COVID-19: Age Differences in Facing the Pandemic
Bibliographic record
Abstract
Literature about the novel Coronavirus (COVID-19) is currently focusing on the potential cognitive and neuropsychiatric sequelae observed in individuals receiving intensive care unit (ICU) treatments. The aim of the present study is to evaluate the differences in cognitive and psychological sequelae of COVID-19 between younger and older adults, regardless of being admitted to the ICU or not. The study involved 299 recovered individuals (from 18 to 90 years old), who underwent a comprehensive cognitive and psychological assessment. Linear regression models were conducted separately for Montreal Cognitive Assessment (MoCA) test and Post-traumatic Stress Disorder Checklist (PCL) scores to investigate the effect of socio-demographic and clinical characteristics on them. Separate linear regression models were then applied sorting participants by age: younger adults (<65 years) and older adults (≥65 years). In the whole sample, PCL scores were predicted by the intensity of care received, by being intubated, and by the persistence of cough after 1 month after hospitalization. Only age had instead an effect on cognition. In younger adults, PCL scores were predicted by the presence of neurological symptoms, by the intensity of care received, and by being intubated; MoCA scores were only predicted by the intensity of care received. No significant associations were found in older adults. Psychological negative effects of the COVID-19 pandemic particularly affect individuals under 65 years old, who also subjectively report cognitive sequelae associated with the infection. Individuals over 65 years old, instead, seem to be free from psychological and cognitive difficulties due to COVID-19.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".