Characteristics of cognitive functioning in persons with chronic sensorineural hearing loss
Bibliographic record
Abstract
Aim to identify the features of cognitive status in chronic sensorineural hearing loss (CNHL) by analyzing the subscales of the Montreal Cognitive Assessment test. Material and methods. The study involved 45 people (aged 44 - 86 years, average age 57.8711.74 years) with chronic sensorineural hearing loss. The study design included assessment of complaints and anamnesis, examination of ENT organs, tonal threshold audiometry (AC-40, Interacoustics, Denmark), tympanometry (AC 226, Interacoustics, Denmark). Cognitive status was assessed using the Montreal Cognitive Assessment tool (MoCA) on seven subscales: Executive and visuospatial function, Naming, Attention, Language, Abstraction, Delayed recall, and Orientation. Results. The average total score for MoCA in persons with chronic SNHL (n=45) was 25.092.86 points out of 30 possible points, which indicates a trend towards a decrease in cognitive function. Cognitive impairments in individuals with SNHL were more pronounced on the subscales Executive and visuospatial function, Language and Delayed recall. Despite rather high average scores on the subscale Executive and visuospatial function, 100% completion was noted less than in half of the subjects in 46.66% (n = 21). Conclusion. Considering the cognitive characteristics of individuals with SNHL may improve the effectiveness of rehabilitation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".