Applicability of a short form of the Speech, Spatial and Qualities of Hearing Scale in 97 individuals with Menière’s disease in a multicenter registry
Bibliographic record
Abstract
Abstract BACKGROUND Menière’s Disease (MD) is a debilitating disorder with episodic and variable ear symptoms. Diagnosis can be challenging and there is no current consensus regarding monitoring of disease progression. A psychometric instrument may aid the assessment of subjective hearing impairment and the associated burden in daily life over the course of the disease. However, evidence in this regard is lacking. OBJECTIVE To evaluate a German-language short form of the Speech, Spatial and Qualities of Hearing Scale (SSQ) for hearing-related disability in MD. METHODS Data was collected from a multicenter prospective patient registry for long-term follow up of MD patients. Hearing was assessed by pure tone and speech audiometry. The applied version of the SSQ contained 17 items. RESULTS 97 consecutive patients with unilateral MD had a mean age of 56.2 ± 5.0 years. 55 individuals (57.3%) were female, 72 (75.0%) were categorized as definite MD. 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 negative correlation with hearing thresholds and a positive correlation with speech recognition scores. CONCLUSIONS The short form of the SSQ provides insight into hearing-specific disability in patients with MD. Thereby, 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".