Understanding Self-reported Hearing Disability in Adults With Normal Hearing
Bibliographic record
Abstract
OBJECTIVES: Despite a diagnosis of normal hearing, many people experience hearing disability (HD) in their everyday lives. This study assessed the ability of a number of demographic and auditory variables to explain and predict self-reported HD in people regarded as audiologically healthy via audiometric thresholds. DESIGN: One-hundred eleven adults (ages 19 to 74) with clinically normal hearing (i.e., audiometric thresholds ≤25 dB HL at all octave and interoctave frequencies between 0.25 and 8 kHz and bilaterally symmetric hearing) were asked to complete the 12-item version of the Speech, Spatial, and Qualities of Hearing Scale (SSQ12) as a measure of self-reported HD. Patient history and a number of standard and expanded measures of hearing were assessed in a multivariate regression analysis to predict SSQ12 score. Patient history included age, sex, history of noise exposure, and tinnitus. Hearing-related measures included audiometry at standard and extended high frequencies, word recognition, otoacoustic emissions, auditory brainstem response, the Montreal Cognitive Assessment, and FM detection threshold. RESULTS: History of impulse noise exposure, speech-intelligibility index, and FM detection threshold accurately predicted SSQ12 and were able to account for 40% of the SSQ12 score. These three measures were also able to predict whether participants self-reported HD with a sensitivity of 89% and specificity of 86%. CONCLUSIONS: Although participant audiometric thresholds were within normal limits, higher thresholds, history of impulse noise exposure, and FM detection predicted self-reported HD.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".