Design and Usability Assessment of a Cognitive Screening Digital Tool on Tablet: AlzVR Project
Bibliographic record
Abstract
Alzheimer's disease (AD) is the first cause of dementia worldwide and represents a public health challenge. Current diagnostic methods still rely on extended interviews and paper tests. We aim to create a novel, quick cognitive-screening tool on a numerical tablet. This program, built and edited with Unity®, runs on Android® for the Samsung Galaxy Tab S7 FE®. Composed of seven tasks inspired by the Mini-Mental Status Examination and the Montréal Cognitive Assessment, it browses several cognitive functions. The architectural design of this tablet application is distinguished by its multifaceted capabilities, encompassing not only seamless offline functionality but also a mechanism to ensure the singularity of data amalgamated from diverse sites. Additionally, a paramount emphasis is placed on safeguarding the confidentiality of patient information in the healthcare domain. Furthermore, the application empowers individual site managers by allowing them to access and peruse specific datasets, thereby enhancing their operational efficacy and decision-making processes. We performed a usability assessment among 24 healthy patients with a final F-SUS score of "excellent". Participants perceived the tool as simple to use and achieved the test in a mean time of 142 seconds, confirming that a short assessment on a numerical tablet is possible.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".