An Initial Validation of Community-Based Air-Conduction Audiometry in Adults With Simulated Hearing Impairment Using a New Web App, DigiBel: Validation Study
Bibliographic record
Abstract
BACKGROUND: Approximately 80% of primary school children in the United States and Europe experience glue ear, which may impair hearing at a critical time for speech acquisition and social development. A web-based app, DigiBel, has been developed primarily to identify individuals with conductive hearing impairment who may benefit from the temporary use of bone-conduction assistive technology in the community. OBJECTIVE: This preliminary study aims to determine the screening accuracy and usability of DigiBel self-assessed air-conduction (AC) pure tone audiometry in adult volunteers with simulated hearing impairment prior to formal clinical validation. METHODS: Healthy adults, each with 1 ear plugged, underwent automated AC pure tone audiometry (reference test) and DigiBel audiometry in quiet community settings. Threshold measurements were compared across 6 tone frequencies and DigiBel test-retest reliability was calculated. The accuracy of DigiBel for detecting more than 20 dB of hearing impairment was assessed. A total of 30 adults (30 unplugged ears and 30 plugged ears) completed both audiometry tests. RESULTS: DigiBel had 100% sensitivity (95% CI 87.23-100) and 72.73% (95% CI 54.48-86.70) specificity in detecting hearing impairment. Threshold mean bias was insignificant except at 4000 and 8000 Hz where a small but significant overestimation of threshold measurement was identified. All 24 participants completing feedback rated the DigiBel test as good or excellent and 21 (88%) participants agreed or strongly agreed that they would be able to do the test at home without help. CONCLUSIONS: This study supports the potential use of DigiBel as a screening tool for hearing impairment. The findings will be used to improve the software further prior to undertaking a formal clinical trial of AC and bone-conduction audiometry in individuals with suspected conductive hearing impairment.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".