Dementia Care : Literature Survey and Design
Bibliographic record
Abstract
The proposed system is geared towards supporting individuals grappling with dementia by introducing a virtual memory application that facilitates the storage and retrieval of memories as memory loss advances. The primary objective is to enhance the lifestyle for both patients with dementia and their caregivers, addressing the isolating and frustrating nature of dementia. Accessible through an application, the system enables users, including patients and their guardians, to effortlessly store and retrieve memories. Each patient is assigned a unique QR code, which, when scanned, provides essential details, including medical condition, personal information, and contact details for guardians. The system is comprised of three main modules. New patients can register on the system by completing a form that collects necessary details. The provided information is then verified and uploaded to the MySQL database. The front end is developed using HTML, CSS, and XML, while the back-end is implemented in Java. By providing an intuitive platform for memory storage, dementia stage analysis, and educational resources, the proposed system aims to alleviate stress and improves interaction between patients and their caregivers. This initiative aligns with research indicating that cognitive stimulation activities may potentially slow the progression of dementia.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".