Bibliographic record
Abstract
The rise of digital tools - online cognitive tests, AI-powered diagnostics, smart watches, and other app-based symptom trackers - offers unprecedented opportunities for the early detection and improved management of conditions like Alzheimer's disease and other dementias. Digital platforms offer a number of advantages over more traditional paper-and-pencil approaches, including increased sensitivity, increased specificity, ease of administration, broader performance measures (e.g. response times, attempt counts, and error types), as well as the number and depth of cognitive domains assessed. We have recently developed a five-minute, web-based cognitive 'screener' for detecting the early signs of dementia. A machine learning approach was used to identify the two most informative cognitive tasks from an initial library of 12 possibilities, covering working memory, attention, reasoning and problem-solving. The evaluation model used 22 features from these tasks, including reaction times and error rates, to predict if an individual was cognitively healthy or potentially impaired. The model was trained on data from over 8,000 healthy individuals and more than 3,000 patients aged 50+. It was further validated with a different group of 800 healthy individuals and 1,000 patients, achieving an accuracy rate of over 80%. In individuals clinically diagnosed with Alzheimer's disease, 100% were accurately flagged for further testing. The results confirm that digital cognitive screeners for dementia offer comparable or superior accuracy relative to their traditional paper-and-pencil counterparts, can be easily scaled for mass deployment, and are a cost-effective and convenient approach to early detection.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".