A Pilot Tool of the Virtual Scenario Initial Dementia Cognitive Screening (VSIDCS) with a Cultural Exhibition for Improving the Standard Traditional Test
Bibliographic record
Abstract
Dementia has become a serious global health problem for older people. In the past, primary screening for dementia was carried out by a paper test. These standard traditional tests (e.g., Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE)) have been used for many years. In addition to paper tests, is there another way to let people have better involvement and emotions during the test procedure? With the advancement of technology, the application of virtual reality (VR) and augmented reality (AR) have changed and improved many medical technologies. However, there are few applications of VR and AR in dementia screening. The purpose of this study was to apply VR and AR to construct a pilot tool for virtual scenario initial dementia cognitive screening (VSIDCS) with a cultural exhibition, to achieve better involvement and emotions in participants. There were three operating interfaces designed for the system: a VR screening interface, cognitive board, and AR recognition interface. There were twenty-four middle-aged people (Female 10 and Male 14 between 50 and 65 years of age and with an average age of 58.7 years) selected for the test. The results of the experiments showed that VSIDCS test scores are consistent with those of the MoCA and MMSE. Additionally, VSIDCS can induce better involvement and emotions than the MoCA and MMSE. Participants showed better enthusiasm and more positive experiences during the VSIDCS test.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".