Digital screening tool for the assessment of cognitive impairment in unsupervised setting—digiDEM-SCREEN: study protocol for a validation study
Bibliographic record
Abstract
INTRODUCTION: Dementia is one of the most relevant widespread diseases, with a prevalence of currently 55 million people with dementia worldwide. However, about 60-75% of people with dementia have not yet received a formal diagnosis. Asymptomatic screening of cognitive impairments using neuropsychiatric tests has been proven to efficiently enhance diagnosis rates. Digital screening tools, in particular, provide the advantage of being accessible without spatial or time restrictions. The study aims to validate a digital cognitive screening test (digiDEM-SCREEN) as an app in the German language. METHODS AND ANALYSIS: This is a multicentre study in Bavaria. Participants are people with mild cognitive impairment, people with dementia in an early stage and cognitively healthy people. Recruitment will take place in specialised diagnostic facilities (memory outpatient clinics). 135 participants are aimed based on a power analysis. Sociodemographic data, diagnosis and results of neuropsychiatric tests (Consortium to Establish a Registry for Alzheimer's Disease, Montreal Cognitive Assessment, digiDEM-SCREEN) will be collected at one point per person via electronic data capturing. The sensitivity, specificity and corresponding cut-off values will be determined based on receiver-operating-characteristic curves. The correlation of the digiDEM-SCREEN test with existing cognitive screening/testing procedures will be analysed. ETHICS AND DISSEMINATION: The study obtained ethical approval from the Ethics Committee of the Julius-Maximilians-Universität of Würzburg (JMU) (application number: 177/23-sc). The test will give feedback about the current cognitive status and possible cognitive impairments that should lead to the users seeking further diagnostic measures by medical professionals. It will be accessible free of charge in established app stores. The results of the validation study will be published in peer-reviewed journals.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.032 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.011 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".