Impact of COVID-19 on functional, cognitive, neuropsychiatric, and health-related outcomes in patients with dementia: A systematic review
Bibliographic record
Abstract
Background: This systematic review analyzes the impact of COVID-19 on dementia patients' functional, cognitive, neuropsychiatric, and health related outcomes. It hypothesizes that dementia patients infected with SARS-CoV-2experience more pronounced deterioration compared to those who are uninfected. Methods: Research from 01/03/2020 to 07/10/2023 was conducted using Medline, Web of Science, and Embase databases, and adhering to PRISMA guidelines and the PICO framework. The study aimed to determine if SARS-CoV-2 infection is associated with worse outcomes in dementia patients. The protocol is registered in PROSPERO (CRD42022352481), and bias was evaluated using the Newcastle-Ottawa Scale. Results: Among 198 studies reviewed, only three met the criteria. Chen et al. (2023) identified higher mortality in SARS-CoV-2-infected dementia patients, while Merla et al. (2023) observed faster cognitive decline in infected individuals with increased hospital admissions. Additionally, Cascini et al. (2022) reported an increased risk of infection and significantly elevated mortality in dementia patients, highlighting comorbidities and antipsychotic medication use as key risk factors. Conclusion: These limited data suggest higher mortality and cognitive decline in dementia patients following COVID-19, underscoring the need for extensive research in this area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".