COVID-19 Accelerated Cognitive Decline in Elderly Patients with Pre-Existing Dementia Followed up in an Outpatient Memory Care Facility
Bibliographic record
Abstract
Introduction: Coronavirus disease 2019 (COVID-19) may affect the cognitive function and activities of daily living (ADL) of elderly patients. This study aimed to establish the COVID-19 effect on cognitive decline and the velocity of cognitive function and ADL changes in elderly patients with dementia followed up in an outpatient memory care facility. Methods: In total, 111 consecutive patients (age 82 ± 5 years, 32% males) with a baseline visit before infection were divided into those who had or did not have COVID-19. Cognitive decline was defined as a five-point loss of Mini-Mental State Examination (MMSE) score and ADL comprising basic and instrumental ADL indexes (BADL and IADL, respectively). COVID-19 effect on cognitive decline was weighted for confounding variables by the propensity score, whereas the effect on change in the MMSE score and ADL indexes was analyzed using multivariate mixed-effect linear regression. Results: COVID-19 occurred in 31 patients and a cognitive decline in 44. Cognitive decline was about three and a half times more frequent in patients who had COVID-19 (weighted hazard ratio 3.56, 95% confidence interval 1.50–8.59, p = 0.004). The MMSE score lowered on average by 1.7 points/year, independently of COVID-19, but it lowered twice faster in those who had COVID-19 (3.3 vs. 1.7 points/year, respectively, p < 0.050). BADL and IADL indexes lowered on average less than 1 point/year, independently of COVID-19 occurrence. Patients who had COVID-19 had a higher incidence of new institutionalization than those who did not have the disease (45% versus 20%, p = 0.016, respectively). Conclusions: COVID-19 had a significant impact on cognitive decline and accelerated MMSE reduction in elderly patients with dementia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".