Development and Validation of a Self-Administered Online Hearing Test
Bibliographic record
Abstract
We describe the development and validation of a self-administered online hearing test, which screens for hearing loss and provides an estimated audiogram. The hearing test computes test results from age, self-reported hearing abilities, and self-assessed pure-tone thresholds. It relies on regression, Bayesian and binary classification, leveraging probabilistic effects of age as well as interfrequency and interaural relationships in audiograms. The test was devised based on development data, collected prospectively in an online experiment from a purposive convenience sample of 251 adult American, Australian, Canadian, and Swiss participants, 58% of whom had hearing loss. Later, we externally validated the hearing test. Validation data were collected prospectively from a representative sample of 156 adult Belgian participants, 15% of whom had hearing loss. Participants completed the hearing test and audiometric assessments at home. The results for the primary screening outcome showed that the hearing test screened for mild hearing losses with a sensitivity of 0.83 [95%-confidence interval (CI): 0.65, 0.96], specificity of 0.94 [CI: 0.89, 0.98], positive predictive value of 0.70 [CI: 0.57, 0.87], and negative predictive value of 0.97 [CI: 0.94, 0.99]. Results for the secondary audiogram estimation outcome showed mean differences between estimated and gold standard hearing thresholds ranging from 2.1 to 12.4 dB, with an average standard deviation of the differences of 14.8 dB. In conclusion, the hearing test performed comparably to state-of-the-art hearing screeners. This test, therefore, is a validated alternative to existing screening tools, and, additionally, it provides an estimated audiogram.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".