Integrated Digit-in-Noise Test: A Rapid Screening Tool for Hearing and Cognitive Function
Bibliographic record
Abstract
Abstract A rapid and easy-to-administer screening tool is essential for community-based detection of hearing loss and cognitive decline. The Integrated Digit-in-Noise Test (iDIN) extends the traditional Digit-in-Noise Test (DIN) by incorporating 2- to 5-digit sequences, as the only test to simultaneous assess and differentiate hearing and cognition. Speech Reception Thresholds (SRTs) are measured as the signal-to-noise ratio (SNR) at which 50% of digits are correctly identified. Specifically, 3-digit SRTs are used for hearing screening, while the difference between backward and forward 3-digit SRTs (SRT3b-3) serves as an indicator of cognitive function. In this study, 601 community-dwelling participants with potential but undiagnosed hearing loss were recruited (mean age 76.0 ± 8.8 years; education 6.4 ± 4.4 years; MoCA 21.5 ± 6.4). In terms of hearing screening, the average 3-digit SRT was -4.3 ± 7.0 dB SNR, with a cutoff of -7.7 dB SNR for hearing loss detection (35 dB HL in better ear) (sensitivity 0.85, specificity 0.73). For cognitive screening, the mean SRT3b-3 was 5.03 ± 7.32 dB SNR. Using MoCA thresholds of 21/22 for MCI, the optimal SRT3b-3 cutoff was 3.3 dB (sensitivity 0.74, specificity 0.76); for dementia (MoCA 15), the cutoff was 5.5 dB (sensitivity 0.83, specificity 0.77). No significant correlation was found between SRT3b-3 and better ear hearing levels, indicating that iDIN can effectively be used for cognitive screening in older adults with hearing impairment. In conclusion, iDIN shows promise as a quick, dual-purpose screening tool for early detection of hearing and cognitive issues in community settings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".