Measuring Tinnitus Loudness Using Constrained Psychophysical Scaling
Bibliographic record
Abstract
PURPOSE: We measured tinnitus loudness using a new method of psychophysical scaling with the aim of introducing a potentially useful new procedure to the literature. METHOD: Fourteen adults reporting tinnitus were trained to use a standardized loudness scale, and then they used that response scale to assess loudness of nonstandard stimuli and of their tinnitus. We also measured tinnitus loudness and pitch using a computer-based matching procedure, and we measured the impact of tinnitus on daily living using the Tinnitus Handicap Inventory (THI; C. W. Newman, G. P. Jacobson, & J. B. Spitzer, 1996) for those 14 individuals and an additional 2 participants. Results and Conclusions Our 14 trained participants judged loudness similarly to normal hearing participants for pure tones at normal hearing, nontinnitus frequencies-implying that their judgments of tinnitus loudness were valid. Constrained scaling of tinnitus loudness yielded measurements that were substantially greater than the sensation level of sounds matched to tinnitus loudness. Our total of 16 participants fell into 2 groups on the basis of hearing loss, extent of abnormal loudness growth at the tinnitus frequency, and several aspects of tinnitus experience. Finally, as previously found, there was little correlation between tinnitus loudness, no matter how measured, and the impact of tinnitus on daily life as measured by the THI.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".