AudioAid creation using Open-Source Audiometric Notched Hearing as a Therapy for Tinnitus Relief
Bibliographic record
Abstract
Tinnitus affects millions of people around the world, and, unfortunately, there are very few effective treatment options, particularly for those without access to specialized audiological care. AudioAid is a new, innovative, open-source, comprehensive web-based platform that provides individual patients with personalized notched auditory stimulation therapy through any standard web browser and headphones. The platform utilizes a diagnostic module that identifies the frequency of the individual's tinnitus, and a selection of custom therapeutic pathways that include nightly listening sessions that contain white noise filtered with spectral notches centred on the patient's tinnitus frequency. AudioAid is built on modern web development technology, specifically React.js and serverless architecture, which allows us to eliminate traditional barriers of access for tinnitus treatment services since the platform does not require any specialized hardware or a therapist's supervision. The diagnostic component generates a dual assessment of the laterality, subjective intensity, and dominant frequency characteristics of an individual's tinnitus, and the therapeutic component generates dermatological filtered white noise listening sessions, each meant for passive listening at night. Components to foster user engagement, motivate progress, monitor clinical and treatment outcomes, and encourage adherence to their listening sessions are included. Dashboards of progress tracking, automated reminders to encourage adherence, and self-assessment are identified as user engagement features. This democratized access and evidence-based solution addresses a very real separation between the clinical advances made in notched auditory stimulation studies and the accessibility of those advances for patients around the world. AudioAid ideally represents how a scalable, low-cost intervention can be provided, which allows individuals to manage their tinnitus symptoms using evidence-based techniques to educate them on the use of spectral filtering that, overall, has the potential to change the way we deliver auditory healthcare services to underserved populations worldwide.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.013 |
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".