Tablet and web-based audiometry to screen for hearing loss in adults with cystic fibrosis
Bibliographic record
Abstract
INTRODUCTION: Individuals with chronic lung disease (eg, cystic fibrosis (CF)) often receive antimicrobial therapy including aminoglycosides resulting in ototoxicity. Extended high-frequency audiometry has increased sensitivity for ototoxicity detection, but diagnostic audiometry in a sound-booth is costly, time-consuming and requires a trained audiologist. This cross-sectional study analysed tablet-based audiometry (Shoebox MD) performed by non-audiologists in an outpatient setting, alongside home web-based audiometry (3D Tune-In) to screen for hearing loss in adults with CF. METHODS: Hearing was analysed in 126 CF adults using validated questionnaires, a web self-hearing test (0.5 to 4 kHz), tablet (0.25 to 12 kHz) and sound-booth audiometry (0.25 to 12 kHz). A threshold of ≥25 dB hearing loss at ≥1 audiometric frequency was considered abnormal. Demographics and mitochondrial DNA sequencing were used to analyse risk factors, and accuracy and usability of hearing tests determined. RESULTS: Prevalence of hearing loss within any frequency band tested was 48%. Multivariate analysis showed age (OR 1.127; (95% CI: 1.07 to 1.18; p value<0.0001) per year older) and total intravenous antibiotic days over 10 years (OR 1.006; (95% CI: 1.002 to 1.010; p value=0.004) per further intravenous day) were significantly associated with increased risk of hearing loss. Tablet audiometry had good usability, was 93% sensitive, 88% specific with 94% negative predictive value to screen for hearing loss compared with web self-test audiometry and questionnaires which had poor sensitivity (17% and 13%, respectively). Intraclass correlation (ICC) of tablet versus sound-booth audiometry showed high correlation (ICC >0.9) at all frequencies ≥4 kHz. CONCLUSIONS: Adults with CF have a high prevalence of drug-related hearing loss and tablet-based audiometry can be a practical, accurate screening tool within integrated ototoxicity monitoring programmes for early detection.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".