Feasibility and accuracy of a game-based automated audiometric application (Aud•It) for testing the hearing sensitivity of children and adults
Bibliographic record
Abstract
This study explored the accuracy and the feasibility of a game-based automated hearing testing app (Aud⋅It) that utilized multiple novel strategies to ensure test accuracy. The effects of age and cognitive abilities on participants’ ability to complete Aud⋅It were also examined. Aud⋅It employs strategies including the three-alternative-forced-choice psychophysical paradigm and pairing the acoustic signals with visual alerts to lower the probability of random guessing. A total of 131 participants aged between 3 and 82 years were recruited. The app was implemented on iPads using ER1 insert earphones calibrated to ANSI standards. Thresholds were tested using Aud⋅It and conventional audiometry with test order counterbalanced. Tympanometry and otoacoustic emissions were used to check middle ear and outer hair cell functions. The Montreal Cognitive Assessment was used to screen the cognitive abilities of adult participants. For children aged ≥4 years and adults without cognitive impairment, 95% of their thresholds obtained using Aud⋅It and conventional audiometry agreed within 5 dB. Strong and significant correlation between the audiometric measures demonstrated high concurrent validity. Interclass correlation and Cronbach’s alpha values also indicate excellent agreement. Large threshold differences in most children aged 3 years and older adults with cognitive impairment, however, suggest they should be tested using conventional audiometry by trained audiologists. Aud⋅It is a viable tool for screening children ≥4 years of age and adults, who do not have difficulties understanding test instructions. Future studies will examine if the same candidacy criteria are valid for populations in low-to-middle-income countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".