iHear: Canadian medical student based hearing assessment program for grade school children using a tablet audiometer
Bibliographic record
Abstract
PURPOSE: To evaluate the progress and challenges of a hearing screening program as well as review the incidence of pediatric hearing loss in grade school children participating in this program. METHODS: Medical students from the University of Ottawa established iHear, a grade school hearing assessment program that uses novel tablet audiometry. Over 3 years, children in grades 1 and 2 were assessed and those found to have abnormal results on iHear assessment were then referred to audiology for formal testing, and to otolaryngology if needed. RESULTS: From 2014 to 2017, 753 children aged 5-9 years old were assessed for hearing loss. Mean age of participants was 6.7 years, 51.9% of whom were female. Of the children assessed, 86 (11.4%) had abnormal results and 6 (0.8%) had inconsistent results, necessitating 92 referrals for assessment by a professional audiologist. Of the 65 participants who completed secondary audiologic assessment, 54 (83.1%) were normal and 11 (16.9%) had a definitive hearing loss or abnormal tympanometry. A total of 32 children were lost to follow-up. A total of 118 medical students were involved in the iHear program. CONCLUSIONS: Hearing loss in grade school populations continues to go undetected across Canada. Programs such as iHear demonstrate that gaps in the provision of hearing assessment can be filled effectively by medical students equipped with tablet audiometry. Medical student exposure to audiology and otolaryngology increased through the iHear program.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".