MONTREAL COGNITIVE ASSESSMENT (HEARING IMPAIRED) (GERMAN VERSION) IN GERIATRIC COGNITIVE SCREENING
Bibliographic record
Abstract
Introduction: Cognitive screening is a crucial part of the geriatric assessment. Up to now all current test rely on the subjects ability to follow auditory verbal instructions and therefore present a major pit-fall in the assessment of senior patients with a hearing loss leading to a probable false negative test result due to misunderstanding of the oral commands and requirements. Our objective was to adapt the “Development of a Cognitive Screening Test for the Severely Hearing Impaired” by Chung J, Shipp D, Friesen L, Black S, Masellis M, Lin V for the German language and to introduce it as a standard cognitive screening in our geriatric assessment. Method: Subjects were recruited from our geriatric clinic. All volunteers first underwent a battery of cognitive screening tools (MMSE, clock completion test)in a standard environment and a hearing assessment (MAT). After that all wereadministered the MoCA-HI. 50 normal hearing and 100 hearing impaired(>40 dB(A)) subjects were tested. As an additional cognitive test the CERAD test battery was performed on all subjects in a hearing adjusted environment. Results: There was a significant correlation between the test results of the MoCA-HI and the CERAD plus battery were as the normal cognitive test showed an inclination towards lower scores in the hearing impaired subjects. The MoCA-HI was introduced to our geriatic clinic as our standard cognitive screening test. Conclusion: The MoCA-HI should be used in cognitive screening in geriatric patients to avoid false negative scores due to 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".