CheckMyFit
Bibliographic record
Abstract
Putting on hearing aids (HAs) is a regular and crucial task for every hearing aid wearer. A sub-optimal insertion can impact user adoption and audiological benefit. Ability to visually evaluate the insertion can be helpful to achieve a proper physical fit of hearing aids or similar devices, but this is currently a challenging task. In this work we present CheckMyFit, a smartphone-based, automated solution enabling users to quickly take a photo of their hearing aid placement, and compare it with a reference ideal insertion. To evaluate the tool's usability and potential benefit we conducted two user studies: a) a pilot lab study with 7 participants, and b) a field study with 17 participants. In the two-week field study, older participants with no prior hearing aid experiences were instructed on hearing aid insertion remotely and performed daily insertions independently at home. We found that CheckMyFit is easy and quick to use for almost all participants. Ear-photo-aided insertions tend to have higher quality than insertions without the tool. This correlation was significant and persisted throughout the 2 weeks of the study, and is retained after a short break. This suggests that CheckMyFit tool can provide real-world benefit to new users learning to insert their hearing aids. We also used CheckMyFit to remotely facilitate the field study, demonstrating its potential usefulness in tele-medicine.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.104 | 0.031 |
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".