Unveiling the potential of the Digits-in-Noise test as a hearing screening tool for older adults with cognitive impairment
Bibliographic record
Abstract
BackgroundThe Digits-in-Noise (DIN) test is recognized as a promising hearing screening tool due to its feasibility and reliability, particularly in noisy environments. Although endorsed by the World Health Organization for general population hearing screening, it has not been validated in older adults with cognitive impairment, such as mild cognitive impairment (MCI), and dementia, including Alzheimer's disease.ObjectiveTo address this gap, this study aimed to evaluate diagnostic accuracy of DIN compared to pure-tone audiometry, marking the first validation of the DIN test in this specific group.MethodsParticipants with MCI and dementia were recruited from memory clinics. Each participant underwent an audiologic evaluation, including the Hearing Handicap Inventory for Elderly, pure-tone audiometry, and smartphone-based DIN test. Additionally, the Montreal Cognitive Assessment, was administered.ResultsAmong 93 adults (mean age 71.9), an optimal speech reception threshold (SRT) cutoff of -3.5 dB yielded 90.5% sensitivity and 50% specificity for detecting moderate hearing loss. The area under the curve was 0.649 for mild hearing loss and 0.746 for moderate. A significant weak positive correlation was observed between SRT and pure tone average (ρ= 0.35, p < 0.001)ConclusionOur findings underscore the potential of the DIN test in detecting disabling hearing loss among cognitively impaired individuals, which warrants immediate hearing intervention to improve their quality of life.Trial RegistrationThai Clinical Trials Registry (TCTR20221222004), https://www.thaiclinicaltrials.org/.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".