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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".