Evaluation of the MoCA-HI in cognitive impaired patients
Bibliographic record
Abstract
Introduction Hearing loss and cognitive impairment often occur simultaneously in old age. Most cognitive test batteries are auditory-based with the risk of false-positive results. Therefore, Dawes developed a non-auditory version of the original MoCA adapted for hearing-impaired with three different subtasks. This version has already been studied in cognitive healthy subjects. The aim of the study was to evaluate the German version of the MoCA-HI in cognitive impaired patients with and without hearing loss. Material and Methods 81 patients without or with only a slight hearing loss (NH, 4PTAB ≤40dB) and 110 patients with a moderate or severe hearing loss (SH, 4PTAB >40dB) were included. 90 patients (81.88 years, SD6.56) had MCI (mild cognitive impairment) and 101 (83.47 years, SD6.38) showed the criteria of dementia according to the S3-DGN-guidelines. Further the GPCOG, a depression (GDS-15) and a stress (PSQ) questionnaire were applied and sociodemographics were assessed. Results In the MoCA-HI total score, dementia patients (15.23, SD5.27) performed significantly worse than MCI patients (18.61, SD4.06) (p<.001). This was also true for all subtests except for the abstraction task, where two items have to be assigned to a common category and where no difference could be detected between MCI and dementia patients (p=.44). Hearing-impaired performed significantly better on the three subtasks of the MoCA-HI than on those of the original MoCA (p<.001), while this was not true for normal hearing subjects (p=.363). Discussion The MoCA-HI test is a suitable screening tool for the detection of MCI or dementia in hearing-impaired and should replace the currently used MoCA in the clinical routine. Publication History Article published online: 12 May 2023 Georg Thieme Verlag 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".