Comparison of Tinnitus Loudness Measures: Matching, Rating, and Scaling
Bibliographic record
Abstract
Purpose Chronic tinnitus ("ringing in the ears") is a phantom auditory perception with no cure. A goal of treatment is often to reduce the loudness of tinnitus. However, tinnitus loudness cannot be measured objectively. It is most commonly assessed by obtaining a loudness match (LM) with a pure tone and by using a numeric rating scale (NRS). Constrained loudness scaling (CLS) is a more recent measure of tinnitus loudness that utilizes auditory training of a fixed loudness scale to guide tinnitus loudness judgments. The purpose of this study was to compare results using these 3 measures of tinnitus loudness. Method This study obtained tinnitus loudness measures of LM, NRS, and CLS with 170 participants. These participants are part of a larger study obtaining repeated measures over 6 months. Only baseline data are presented. Results Correlations between all measures were weak to moderate: LM versus CLS ( r = .46), CLS versus NRS ( r = .49), and LM versus NRS ( r = .38). Conclusion Further systematic research is needed to more fully understand the relationships between these different measures and to establish a valid measure of tinnitus loudness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".