Diagnosis of dementia in hearing-impaired subjects using the MOCA-HI
Bibliographic record
Abstract
As dementia diagnostic tools such as the MoCA screening test are mainly auditory-based, the risk of false-positive diagnosis in the presence of a moderate to severe hearing impairment cannot be ruled out. Therefore, a visual-based MoCA-HI has recently been developed in English. The aim of the present study was to assess a German version of the MoCA-HI in cognitive healthy subjects aged 60 or more with and without hearing loss. After translation, the MoCA-HI was tested on 94 subjects with normal or slightly impaired hearing (NH) and on 81 subjects with moderate or severe hearing loss (SH) aged 60 to 97 (M: 71.52). Additionally, cognitive testing was performed with the standard MoCA and the GPCOG and socioeconomic as well as psychosocial data (GDS-15) were recorded. In 115 patients a retest was done after a period of at least 4 weeks. A higher age (p<.001), male gender (p=.011), and lower education level (p<.001) were associated with a lower MoCA-HI total score. After accounting for these factors, no significant difference was found between NH and SH in the MoCA-HI total score (p=.550), the cognitive subdomains (p≥.494), or the three adapted items of the MoCA-HI (p≥.227). Retest reliability was high with a correlation of 0.844 (p<.001). In the German MoCA-HI, the previously described difference between NH and SH could not be detected anymore. Currently, cognitively impaired normal hearing and hearing-impaired subjects are included in the study. The aim is to assess normative data adapted to age, gender and educational level, which allow to use the MoCA-HI in clinical practice in the long-term. Publication History Article published online: 24 May 2022 © 2022. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/). Georg Thieme Verlag Rüdigerstraße 14, 70469 Stuttgart, Germany
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".