iPad Audiometry in Canada's North: A Portable and Cost‐Effective Method for Hearing Screening
Bibliographic record
Abstract
Objectives: Access to hearing health care is limited in many parts of the world. Specifically, many risk factors for hearing loss are present in the First Nations people of Canada's North. No research has been done to assess this populations hearing in over 3 decades. The aims of the study were: (1) Determine the prevalence of hearing loss in children in Baffin Island. (2) Demonstrate the use of asynchronous tele‐audiometry. (3) Conduct a cost‐benefit analysis of iPad audiometry in the Canadian Arctic. Methods: iPad audiometers were used to test hearing in 220 children ages 5‐11 years in Iqaluit, Nunavut, during 1 week in January 2014. Air conduction pure tones were obtained from each ear at frequencies of 500, 1000, 2000, and 4000 Hz. Children with hearing loss greater than 25 dB in at least 1 frequency were considered to have failed the hearing test and will be further tested with standard sound booth audiometry. Results: Preliminary analysis reveals a hearing loss prevalence of 15.5%. These children then received standard testing by an audiologist using standard sound booth audiometry and the results analyzed. A cost‐benefit analysis assessed the use of iPad audiometry in this remote location. Conclusions: This is the first study in over 30 years assessing the hearing of children in this region, and the first telemedicine audiometry in Canada using iPads. This type of testing is valuable for providing hearing health care for high risk populations in rural and remote areas at an affordable cost.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".