P3‐275: Identifying dementia in deaf sign language users
Bibliographic record
Abstract
Tests used to identify cognitive disorders in users of spoken languages are unsuitable for deaf people who use sign languages. Establishing the norms of healthy ageing in the deaf sign language community in respect of cognitive and linguistic functioning is a necessary precursor to the development of assessment tools that might be used to detect unusual changes associated with dementia. We established the parameters of normative cognitive ageing in deaf people, using a new cognitive screening test specifically developed in British Sign Language (BSL). This test is now available to patients at the National Hospital for Neurology and Neurosurgery in England. The tests, with instructions entirely in BSL using standardized videoformat, were developed and piloted using a similar format to the Addenbrokes Cognitive Examination (ACER) (Mioshi et al., 2005), Montreal Cognitive Assessment (MOCA) (Nasreddine et al., 2005) and mini-mental state examination (Folstein et al., 1975), with test domains sampling memory, visuospatial, language and executive function abilities, as well as orientation to time and space. Adaptations were made such as using phonological fluency tasks based on handshape rather than the letter ‘S’ which favours English speakers, naming items were adapted so only those items which were non-iconic were used and all English requirements such as writing a sentence and repeating English sentences were adapted into BSL sentences. Normative data was collected from 226 participants aged 50-89 years during an annual holiday for deaf older people. Details about test development will be presented with preliminary results showing changes in test performance across age cohorts, correlation with non-verbal intellectual ability and plans for the collection of data from patients with cognitive disorder and dementia. Overall the findings demonstrate the benefits of testing for cognitive function in deaf BSL signers using assessments developed specifically for sign language rather than assessments based on spoken language. We conclude that it is optimal to test for cognitive function in deaf BSL signers without using assessments that are based heavily on spoken language.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".