A Cognitive Screening Test for detecting Alzheimer’s Disease Dementia in the Deaf older adults in Austria, and Greece: the De‐Sign Erasmus+ project
Bibliographic record
Abstract
Abstract Background Until now there is a lack of cognitive screening tests for Deaf older adults to detect Alzheimer’s Disease Dementia (ADD) across Europe, as well as limited access to the therapeutic methods imposed. Therefore, the Deaf population has little access to dementia services. Method To fill this gap, the current Erasmus+ project aims to adapt the first dementia screening test for Deaf people in Austria and Greece being initially available for the British Deaf older adults and therefore, provide a valid and reliable tool to detect cognitive deficits by means of ADD in this population. Result For the project’s purposes, a group of experts will translate and adapt the CST in Austrian (ÖGS) and Greek sign languages (GSL), and after piloting the new CST versions, they will be administered in approximately 100 Deaf older adults in each country. Additionally, the following tests will be also administered: the Montreal Cognitive Assessment (MoCA), the Verbal Learning and Memory Test, and the Digit Span (forwards and backwards). To assess the psychometric properties of the new versions of CST, internal reliability, test retest reliability, as well as concurrent validity will be calculated. Finally, a digital web‐based platform including tests’ administration guidelines along with the test per se will be developed. To disseminate the project’s results, detailed seminars on the test administration as well as the use of the platform will be implemented to experts from the fields of dementia and sign languages. Conclusion To introduce dementia screening as well as ADD detection in the Deaf in Austria and Greece.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".