Psychometric Properties of a 17-Item German Language Short Form of the Speech, Spatial, and Qualities of Hearing Scale and Their Correlation to Audiometry in 97 Individuals with Unilateral Menière’s Disease from a Prospective Multicenter Registry
Bibliographic record
Abstract
Background/Objectives: Menière’s disease (MD) is a debilitating disorder with episodic and variable ear symptoms. Diagnosis can be challenging, and evidence for therapeutic approaches is low. Furthermore, patients show a unique and fluctuating configuration of audiovestibular impairment. As a psychometric instrument to assess hearing-specific disability is currently lacking, we evaluated a short form of the Speech, Spatial, and Qualities of Hearing Scale (SSQ) in a cohort of patients with MD. Methods: Data was collected in the context of a multicenter prospective patient registry intended for the long-term follow up of MD patients. Hearing was assessed by pure tone and speech audiometry. The SSQ was applied in the German language version with 17 items. Results: In total, 97 consecutive patients with unilateral MD with a mean age of 56.2 ± 5.0 years were included. A total of 55 individuals (57.3%) were female, and 72 (75.0%) were categorized as having definite MD. The average total score of the SSQ was 6.0 ± 2.1. Cronbach’s alpha for internal consistency was 0.960 for the total score. We did not observe undue floor or ceiling effects. SSQ values showed a statistically negative correlation with hearing thresholds and a statistically positive correlation with speech recognition scores of affected ears. Conclusions: The short form of the SSQ provides insight into hearing-specific disability in patients with MD. Therefore, it may be informative regarding disease stage and rehabilitation needs.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".