USING THE MONTREAL COGNITIVE ASSESSMENT IN MILD AGE-RELATED HEARING LOSS
Bibliographic record
Abstract
Abstract Age-related hearing loss (ARHL) is a modifiable risk factor for dementia. There is a need for early detection of cognitive concerns in older adults with ARHL. The Montreal Cognitive Assessment (MoCA) is a well-validated screening tool that has been used to screen cognition in the ARHL population. However, few studies have examined the value of both MoCA total score (MoCA-TS) and MoCA-Memory Index Score (MoCA-MIS) score in ARHL and its relation to complex hearing functions, especially in those with mild hearing loss. We administered MoCA on 15 older adults with mild-to-moderate ARHL and 15 matched normal hearing controls. MoCA-TS and MoCA-MIS were calculated. Participants also completed the Quick Speech-in-Noise (QuickSiN) test and the Hearing Handicap Inventory for Adults (HHIA). Relative to controls, worse scores were obtained in the mild ARHL group on MoCA-TS (p =.031) and MoCA-MIS (p =.039). Bivariate correlations revealed a significant negative association between QuickSiN and MoCA-TS scores (p <.001), with similar trends on MoCA-MIS (p =.051). When the peripheral hearing measure was controlled using partial correlations, the negative correlation between QuickSiN and MoCA-TS remained (p =.004). The correlations of MoCA-TS and MoCA-MIS with HHIA were not significant. Our findings reveal changes in global cognition beyond encoding memory in mild ARHL. Additionally, our findings suggest that complex listening functions assessed using QuickSiN in related to overall performance on MoCA.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".