Frequency of Audiology Testing Among Individuals with Osteogenesis Imperfecta and Suggestions for Improving Audiogram Participation
Bibliographic record
Abstract
Background: Osteogenesis imperfecta (OI), characterized by brittle bones and frequent fractures, often manifests with earlier onset hearing loss compared with the general population. Purpose: This study aims to assess the frequency of audiology testing in OI individuals and evaluate a portable audiometry device to enhance audiogram participation. Research Design: This is a prospective observational study. Study Sample: Ninety-seven participants were prospectively enrolled. Data Collection and Analysis: Participants underwent a one-time audiology test using SHOEBOX Audiometry Pro with RadioEar DD450 circumaural headphones (Clearwater Clinical Limited, Ottawa, ON, Canada). Hearing loss was defined as having a pure tone threshold (PTT) of ≥25 dB at one or more tested frequencies. Results: Most participants (54/97) reported undergoing professional audiology testing less often than once every 2 years. The most common reported reason for infrequent testing was because patients did not perceive issues with their hearing, even if hearing loss was subsequently found during screening. Seventy-one percent (69/97) of participants had hearing loss (PTT ≥ 25 dB) at one or more frequencies. Conclusions: Using a portable audiometry device in OI clinics could facilitate early hearing loss detection and improve follow-up care, enhancing quality of life.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".