Evaluation of the non-auditory neurocognitive test MoCA-HI for hearing-impaired
Bibliographic record
Abstract
Background Since hearing loss and cognitive decline often co-occur among older adults, a cognitive screening test suitable for hearing-impaired people is of high clinical relevance. We report the first evaluation of a German language version of the Montreal Cognitive Assessment—Hearing Impaired version (MoCA-HI). Objective The aim of the present study was to compare cognitively healthy participants with and without hearing loss, to examine the impact of age, sex, educational level and degree of hearing impairment on the German MoCA-HI performance, and to develop normative data. Material and methods The German MoCA-HI was tested in 94 participants with normal or mild hearing impairment (group 1: 4PTA ≤ 40 dB on the better hearing ear) and 81 participants with moderate to profound hearing loss (group 2: 4PTA > 40 dB on the better hearing ear). Additionally, all participants performed the standard MoCA (version 8.2). Results No significant group difference between group 1 and 2 was found in the MoCA-HI total score (p = 0.05). In contrast, group 1 performed significantly better than group 2 on the standard MoCA (p < 0.001). There was no difference between the MoCA and the MoCA-HI performance in group 1 (p = 0.12), whereas individuals of group 2 performed significantly better on the MoCA-HI than on the standard MoCA (p < 0.001). Test-retest reliability of the MoCA-HI was high (p < 0.001). Higher age (p < 0.001), male sex (p = 0.009) and lower education (p < 0.001) were associated with a lower overall MoCA-HI score. Based on the demographic data normative data were developed by a regression-based approach. Conclusion The MoCA-HI is a cognitive screening test which is suitable for people with hearing impairment.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".