Tinnitus Assessment and Management: A Survey of Practicing Audiologists in the United States and Canada
Bibliographic record
Abstract
BACKGROUND: Tinnitus is a prevalent auditory disorder that can become severely debilitating. Despite decades of investigation, there remains no conclusive cure for tinnitus. Clinical practice guidelines (CPGs) are available for assessing and managing tinnitus. Even though such guidelines have been available for several years, the degree that audiologists adhere to them has remained unexplored. PURPOSE OF STUDY: To determine what clinical practices are commonly used by audiologists in the assessment and management of the patient population with tinnitus, we administered an online survey to audiologists practicing in the United States and Canada. RESULTS: = 61), 70% were from the United States and 30% were from Canada. The audiologists represented a wide range of clinical experience (1-35 years). On average, those who completed the survey were relatively confident in their ability to assess and manage tinnitus patients indicated by a 0 to 100 Likert scale, with 0 representing no confidence (mean 72.5, ± 21.5 standard deviation). The most commonly reported tinnitus assessment tools were pure tone audiogram (0.25-8 kHz), administration of standardized questionnaires, and tinnitus pitch and loudness matching. Approximately half (55%) of audiologists indicated they include otoacoustic emissions, while less audiologists (<40%) reported measuring high-frequency thresholds, minimum masking levels, or loudness discomfort levels. The most common recommendation for tinnitus patients was amplification (87%), followed by counseling (80%) and sound therapy (79%). CONCLUSION: Few audiologists administer a truly comprehensive tinnitus assessment and ∼20% indicated not recommending counseling or sound therapy to manage tinnitus. The results are discussed in the context of what is explicitly indicated in published CPGs, professional organization recommendations, and recent findings of peer-reviewed literature.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".