Does Cognitive Function Affect Performance and Listening Effort During Bilateral Wireless Streaming in Hearing Aid Users?
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Wireless streaming technology (WT), designed to transmit sounds directly from a mobile phone to hearing aids, was developed to enhance the signal-to-noise ratio. However, the advantages of WT during phone use and the specific demographic that can fully benefit from this technology has not been thoroughly evaluated. We aimed to investigate the benefits and identify predictive factors associated with bilateral wireless streaming among hearing aid users. SUBJECTS AND METHODS: Eighteen adults with symmetrical, bilateral hearing loss participated in the study. To assess the benefits of wireless streaming during phone use, researchers assessed sentence/word recognition and listening effort in two scenarios: a noisy background with WT turned "OFF" or "ON." Listening effort was evaluated through self-reported measurements. Cognitive function was also assessed using the Montreal Cognitive Assessment (MoCA) score. RESULTS: Participant mean age was 57.3 years (range 27-70), and the mean MoCA score was 27.0 (23-30). The activation of WT demonstrated a significant improvement in the sentence/word recognition test and reduced listening effort. The MoCA score showed a significant correlation with WT (ρ=0.59, p=0.01), suggesting a positive association between cognitive function and the benefits of WT. CONCLUSIONS: Bilateral wireless streaming may enhance sentence/word recognition and reduce listening effort during phone use in hearing aid users, with these benefits potentially linked to cognitive function.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".