Accuracy of Mobile‐Based Audiometry in the Evaluation of Hearing Loss in Quiet and Noisy Environments
Bibliographic record
Abstract
Objectives (1) To compare the accuracy of 2 previously validated mobile-based hearing tests in determining pure tone thresholds and screening for hearing loss. (2) To determine the accuracy of mobile audiometry in noisy environments through noise reduction strategies. Study Design Prospective clinical study. Setting Tertiary hospital. Subjects and Methods Thirty-three adults with or without hearing loss were tested (mean age, 49.7 years; women, 42.4%). Air conduction thresholds measured as pure tone average and at individual frequencies were assessed by conventional audiogram and by 2 audiometric applications (consumer and professional) on a tablet device. Mobile audiometry was performed in a quiet sound booth and in a noisy sound booth (50 dB of background noise) through active and passive noise reduction strategies. Results On average, 91.1% (95% confidence interval [95% CI], 89.1%-93.2%) and 95.8% (95% CI, 93.5%-97.1%) of the threshold values obtained in a quiet sound booth with the consumer and professional applications, respectively, were within 10 dB of the corresponding audiogram thresholds, as compared with 86.5% (95% CI, 82.6%-88.5%) and 91.3% (95% CI, 88.5%-92.8%) in a noisy sound booth through noise cancellation. When screening for at least moderate hearing loss (pure tone average >40 dB HL), the consumer application showed a sensitivity and specificity of 87.5% and 95.9%, respectively, and the professional application, 100% and 95.9%. Overall, patients preferred mobile audiometry over conventional audiograms. Conclusion Mobile audiometry can correctly estimate pure tone thresholds and screen for moderate hearing loss. Noise reduction strategies in mobile audiometry provide a portable effective solution for hearing assessments outside clinical 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 | 0.004 | 0.018 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".