Comparison of Conventional Audiometry with a Game-Based Audiometric Application for Screening the Hearing Thresholds of Children and Adults
Bibliographic record
Abstract
Abstract Background This study aimed to compare the accuracy and the feasibility of a low-cost automated game-based hearing screening application (Aud⋅It) with conventional audiometry to screen the hearing thresholds of children and adults. Concurrent validity between the two audiometric tests was also explored.Methods Aud⋅It was designed to use a three-alternative choice paradigm to test hearing thresholds and its transducer output accuracy was tested and checked extensively to ensure the conformance to the expected levels of insert earphones specified in ANSI/ASA S3.6: 2010 (2018). It is also equipped with a calibration function that can allow user calibration of other types of transducers (e.g., supraural headphones). A total of 131 children and adults aged between 3 and 82 were recruited for this study. Hearing thresholds were measured using Aud⋅It and conventional audiometry with the order of the tests counterbalanced and the differences between the thresholds were compared for each participant at corresponding frequencies and test ear. Tympanometry and otoacoustic emissions were used to check the middle ear and the outer hair cell functions. The Montreal Cognitive Assessment was used to screen the cognitive abilities of the adult participants.Results Ninety-five percent of the thresholds obtained using Aud⋅It and conventional audiometry agreed within 5 dB, which is an acceptable clinical variability for audiometry testing. Pearson correlation coefficients indicated a strong and significant correlation between the two audiometric measures (r = 0.977), demonstrating excellent concurrent validity. Interclass correlation [ICC3,k; 0.986 (95% CI = 0.980–0.990)] and Cronbach’s alpha (0.988) values indicate the hearing thresholds obtained using Aud⋅It had excellent agreement with those obtained using conventional audiometry.. Most participants aged 3 years and the participants with lower cognitive ability either could not be tested using Aud⋅It or showed large threshold differences between the two audiometric tests, suggesting that they need to be tested using conventional audiometry by trained audiologists. The average test completion time of Aud⋅It was 7 minutes and 48 seconds.Conclusion Aud⋅It is a viable hearing screening tool for testing children more than four years of age and adults without cognitive impairments.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".