Cognitive screening in hearing-impaired: Validation of the German MoCA-H
Bibliographic record
Abstract
With increasing age, not only sensoric, but also cognitive abilities decline. However, most auditory-based cognitive test batteries cannot clearly distinguish between both. The recently developed Montreal Cognitive Assessment-H (MoCA-H), where two tests were replaced by non-auditory tasks in addition to visual instructions, is also suitable for hearing-impaired subjects. So far, reference data for a German-speaking study population is missing. 346 subjects aged≥60 (mean age 77.18 (SD 9.56)) with MCI (n=79) or with dementia (n=107) and without cognitive impairment (n=160) were included. Cognitive function was assessed using the GPCOG, a detailed medical questionnaire and a comprehensive examination by a neurologist in case of cognitive impairment. Cut-off values for normal cognition, MCI and dementia were determined using the balanced cut-off and the Youden's index. Subjects without cognitive impairment scored with 25.49 (SD 3.01), those with MCI with 20.08 (SD 2.29) and subjects with dementia with 15.8 (SD 3.85) points on average. A score of≤21 showed a sensitivity of 96.3% and a specificity of 90% to distinguish cognitive impairment from dementia. To detect MCI, a cut-off range between 22 and 24 points is recommended with a sensitivity of 97.5% and a specificity of 90%. The MoCA-H is a suitable screening test to distinguish cognitively healthy individuals from those with MCI or dementia. However, it cannot replace a comprehensive neuropsychiatric examination. Publication History Article published online: 19 April 2024 © 2024. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".