Developing an automated Cognitive assessment based on language; CognoSpeak‐ working with an ethnic minority group
Bibliographic record
Abstract
Abstract Background This study aimed to explore the issues around developing a new automated cognitive assessment. Current cognitive screening or stratification tools were typically developed only using white people so having a normative data set from almost exclusively white populations. In this part of the project we are co‐developing an automated cognitive assessment tool; CognoSpeak ( https://cognospeak.github.io/website/ ) with a Somali ethnic minority group. CognoSpeak is a fully automated cognitive assessment tool based in language an interaction. A virtual clinician asking questions and listening to responses using automatic speech recognition and Machine Learning algorithms Methods We recruited 2 members of the Somali community team (Israac) and undertook workshops to explore the themes of dementia and cognitive impairment. We trained the Research champions to use cognitive assessment tools and have piloted these on 20 healthy controls from the Somali community. Participants were assessed using the Montreal Cognitive Assessment (MoCA), Rowland Universal Dementia Assessment Scale (RUDAS) and the Multicultural Cognitive Examination (MCE). Results We have undertaken a pilot study, recruiting 14 females, and 7 males ranging from 34 years to 80 years old (with mean age of 48.8). Participants were all from a non‐English background and spoke English and identified as either Somalian or British Somalian. 76.19%(16/21 scored below MoCA = 26; MoCA. Zero scored below cut off on RUDAS (RUDAS = 22; RUDAS: all the patients scored more than 22 and zero scored below cut off of 70 on MCE and 14.29% (3/21) We will aim to recruit 50 participants to interact with CognoSpeak and have results from the three cognitive screens described above. Conclusion We have trained two research champions with no prior research experience to help recruit Healthy controls to start testing CognoSpeak. Validation of novel cognitive tools needs co‐developed methodologies and culturally appropriate cognitive assessment tools. We will have further data on a larger data set, comparing different cognitive screening tools and our automated tool.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".