Integrated digit in noise test (iDIN) for rapid hearing and cognitive screening: a preliminary exploration
Bibliographic record
Abstract
BACKGROUND: Hearing and cognitive impairments are common amongst older adults, both affecting communication and are not easy to distinguish from each other. OBJECTIVE: To preliminarily evaluate the efficacy of the integrated Digit in Noise Test (iDIN) for rapid screening of hearing and cognitive functions in older adults. DESIGN: This cross-sectional cohort study was conducted at multiple clinical sites. SETTING: Data collection occurred in sound-treated booths and quiet rooms at several outpatient clinics and elderly community centres. SUBJECTS: The study included 107 older adults, aged 58-96, who were long-term residents of Hong Kong and native Cantonese speakers. Participants were selected through convenience sampling. METHODS: Primary outcomes were the speech reception thresholds (SRTs) for 2-, 3- and 5-digit sequences with forward and 3-digit sequences with backward recall measured on iDIN. Hearing level was assessed using pure-tone audiometry. Cognitive function was assessed using the Hong Kong version of the Montreal Cognitive Assessment (HK-MoCA). RESULTS: The 2-digit and 3-digit SRTs effectively distinguished participants with hearing loss, demonstrating high sensitivity (0.815 and 0.908, respectively) and specificity (0.905 and 0.853, respectively). The SRT3b-3f index effectively discriminated between participants who passed or failed the MoCA, with sensitivities of 0.727 and 0.781, and specificities of 0.885 and 0.787 using the two MoCA scoring methods. No significant correlation was found between SRT3b-3f and hearing levels after adjustment for educational background. CONCLUSIONS: iDIN demonstrates significant promise for rapid and effective screening of both hearing and cognitive impairments in older adults.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".