Development of an interactive application for Montreal cognitive assessment test
Bibliographic record
Abstract
Alzheimer’s Disease is a type of dementia that causes problems with memory, thinking and behavior which affects the brain, causing the brain cells to die at a faster rate than normal. It is not a normal part of aging, but one of the greatest known risk factors is increasing age and most of the population with Alzheimer’s disease are usually 65 and older. \nAccording to a statistical appendix from a report published in 2014 which is contributed by Alzheimer’s Disease Association, Singapore. Due to low total fertility rate, Singapore’s native population has over the past 45 years become one of the world’s fastest aging populations. \nCurrently, there is no distinct dementia management policy or national dementia plan, but the Ministry of Health had started a National Dementia Network which is integrated with the nationwide awareness for eldercare in Singapore. \nThis report aims to research on the popular tests which can detect Mild Cognitive Impairment or Alzheimer’s Disease early and analyze to compare the different types of tests. The most effective test will be chosen to be developed into a mobile application which must be easily accessible, and the results are simple to interpret without the assistance of a professional.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".