Computer-Automated Tinnitus Assessment: Noise-Band Matching, Maskability, and Residual Inhibition
Bibliographic record
Abstract
BACKGROUND: Psychoacoustic measures of tinnitus typically include loudness and pitch match, minimum masking level (MML), and residual inhibition (RI). We previously developed and documented a computer-automated tinnitus evaluation system (TES) capable of subject-guided loudness and pitch matching. The TES was further developed to conduct computer-aided, subject-guided testing for noise-band matching (NBM), MML, and RI. PURPOSE: The purpose of the present study was to document the capability of the upgraded TES to obtain measures of NBM, MML, and RI, and to determine the test-retest reliability of the responses obtained. RESEARCH DESIGN: Three subject-guided, computer-automated testing protocols were developed to conduct NBM. For MML and RI testing, a 2-12 kHz band of noise was used. All testing was repeated during a second session. STUDY SAMPLE: Subjects meeting study criteria were selected from those who had previously been tested for loudness and pitch matching in our laboratory. A total of 21 subjects completed testing, including seven females and 14 males. RESULTS: The upgraded TES was found to be fairly time efficient. Subjects were generally reliable, both within and between sessions, with respect to the type of stimulus they chose as the best match to their tinnitus. Matching to bandwidth was more variable between measurements, with greater consistency seen for subjects reporting tonal tinnitus or wide-band noisy tinnitus than intermediate types. Between-session repeated MMLs were within 10 dB of each other for all but three of the subjects. Subjects who experienced RI during Session 1 tended to be those who experienced it during Session 2. CONCLUSIONS: This study may represent the first time that NBM, MML, and RI audiometric testing results have been obtained entirely through a self-contained, computer-automated system designed specifically for use in the clinic. Future plans include refinements to achieve greater testing efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".