Cognitive function following SARS-CoV-2 infection in a population-representative Canadian sample
Bibliographic record
Abstract
SARS-CoV-2 infection is believed to adversely affect the brain, but the degree of impact on socially relevant cognitive functioning and decision-making is not well-studied, particularly among those less vulnerable to age-related mortality. The current study sought to determine whether infection status and COVID-19 symptom severity are associated with cognitive dysfunction among young and middled-aged adults in the general population, using self-reported lapses in executive control and a standardized decision-making task. The survey sample comprised 1958 adults with a mean age of 37 years (SD = 10.4); 60.8% were female. Participants reported SARS-CoV-2 infection history and, among those reporting a prior infection, COVID-19 symptom severity. Primary outcomes were self-reported symptoms of cognitive dysfunction assessed via an abbreviated form of the Barkley Deficits in Executive Functioning Scale (BDEFS) and performance on a validated delay-discounting task. Young and middle-aged adults with a positive SARS-CoV-2 infection history reported a significantly higher number of cognitive dysfunction symptoms (Madj = 1.89, SE = 0.08, CI: 1.74, 2.04; n = 175) than their non-infected counterparts (Madj = 1.63, SE = 0.08, CI: 1.47,1.80; n = 1599; β = 0.26, p = .001). Among those infected, there was a dose-response relationship between COVID-19 symptom severity and level of cognitive dysfunction reported, with moderate (β = 0.23, CI: 0.003–0.46) and very/extremely severe (β = 0.69, CI: 0.22–1.16) COVID-19 symptoms being associated with significantly greater cognitive dysfunction. These effects remained reliable and of similar magnitude after controlling for demographics, vaccination status, mitigation behavior frequency, and geographic region, and after removal of those who had been intubated during hospitalization. Very similar—and comparatively larger—effects were found for the delay-discounting task, and when using only PCR confirmed SARS-CoV-2 cases. Positive SARS-CoV-2 infection history and moderate or higher COVID-19 symptom severity are associated with significant symptoms of cognitive dysfunction and amplified delay discounting among young and middle-aged adults with no history of medically induced coma.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".