Assessment of Cognitive Assistive Technologies in Elderly Patients with Dementia Post-COVID-19 Infection: A Research Proposal
Bibliographic record
Abstract
Introduction: SARS-CoV-2 infection is thought to be implicated in an increased risk for various neurodegenerative diseases given its role in neuroinflammation. As a result, cognitive assistive technologies to monitor the risk of neurodegenerative diseases in those with prior COVID-19 infection and the regular implementation of practices designed to promote synaptic plasticity may decrease the risk of dementia in susceptible populations. It is hypothesized that incorporating frequent use of a mobile application designed to improve memory five times per week in the daily routines of patients aged 65+ who have survived a COVID-19 infection may decrease the prevalence of subsequent dementia. Methods: The goal of this application would be to (i) deliver interventions related to long-term potentiation, and (ii) centralizing patient health data in a singular domain to improve ease of access for caregivers and medical staff. This may be investigated through a two-pronged randomized controlled trial aimed to compare neural and cognitive functioning through repeat neuropsychological exams, magnetic resonance imaging, and electroencephalogram tests over a 6-year timeframe between no-intervention and intervention groups while collecting caregiver and healthcare worker data regarding its efficacy in improving quality of life. Results: It is expected that patients consistently using cognitive assistive technologies in long-term care facilities will experience lower incidence of cognitive decline compared to the control group due to improved maintenance of mental health and the learning of new skills. In addition, with the use of technology, patients may experience increased autonomy and independence, improving their quality of life while simultaneously providing relief to their families and caregivers. Discussion: Some further considerations may include the degree of technological proficiency of the patients during the development process to ensure that patients reap the maximum benefits. Factors including rapid development and testing, funding, and strong technology support systems must be taken into consideration to ensure a seamless transition to increased reliance on technology in post-COVID-19 patients with dementia. Conclusion: Strong evidence indicates technology-based interventions can be used to by dementia patients and their caretakers overcome physical and environmental challenges normally and during future pandemic waves.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".