Post-Acute COVID-19 Effects on Diagnostic Conversion Rates And Standardized Cognitive and Motor Test Scores in a Longitudinal Study of Independent, Community-Recruited Elderly Subjects
Bibliographic record
Abstract
There is considerable concern about the long-term consequences of COVID-19 infection, generally referred to as post-acute sequelae, including declines in cognitive and motor abilities. The degree to which COVID-19 contributes additional burden to normal aging trajectories remains unclear. Additionally, the impact of COVID-19 on subjects under study for age-related neurological diseases is a potential confounder that needs definition. This study investigated whether having had a COVID-19 illness was associated with a differential decline in cognitive or motor function in older adults, utilizing data from the Arizona Study of Aging and Neurodegenerative Disorders (AZSAND) and Brain and Body Donation Program (BBDP), a longitudinal clinicopathological study based in metropolitan Phoenix, Arizona. Subjects were included if they 1) had completed a questionnaire about their experience with COVID-19 illness 2) were classified as cognitively normal at pre-pandemic diagnostic cognitive consensus conferences and 3) had one or more subsequent diagnostic conferences between July 1, 2020 and August 30, 2025. All subjects had serial standardized research-dedicated clinical evaluations including the Montreal Cognitive Assessment (MoCA) and the Unified Parkinson's Disease Rating Scale (UPDRS). Specific objectives were to compare, between those who reported having had or not having had COVID-19, rates of conversion to cognitive impairment or dementia as well as pre-pandemic and final MoCA and UPDRS motor scores. A total of 100 subjects self-reported having had COVID-19 while 71 denied having had it. Their related acute illness severity was generally mild, with only 10% having had hospital treatment and none having required ventilator support. Post-acute symptoms were also mild; only 1 subject reported having "long Covid". Both cognitive and motor performance, as measured by MoCA and UPDRS part 3 scores, declined slightly (not statistically significant) over the study period. Conversion of cognitive diagnosis from normal to impaired or dementia occurred in 26% of those having had COVID-19 and in 32% of those not having had COVID-19; the difference was not significant. All subjects diagnosed with PD at the start of the study were also diagnosed with PD at the end of the study; no subjects converted from not having to having probable PD. Logistic regression analysis indicated that subjects' report of having had COVID-19 was not significantly associated with conversion to cognitive impairment or dementia while greater age, male sex, possession of one or more apolipoprotein E- ɛ4 alleles, and a diagnosis of probable PD all conferred a significantly greater likelihood of conversion. The results suggest that having a mild COVID-19 illness is not associated with greater declines on cognitive or motor screening tests than would be expected from age-related changes alone. Limitations of this analysis include small sample sizes, potential misclassification of COVID-19 status, and reliance on relatively crude clinical metrics that may miss significant functional changes. Additionally, we were unable to separately assess for varying COVID-19 severity dependent on whether or not the subject had been vaccinated, the number and type of vaccinations, and the particular SARS-CoV-2 variants that were circulating at the time of illness.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.001 | 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".