Clinical Validation of a New Tinnitus Assessment Technology
Bibliographic record
Abstract
Current clinical assessment of tinnitus relies mainly on self-report. Psychoacoustic assessment of tinnitus pitch and loudness are recommended but methods yield variable results. Herein we investigated the proposition that a previously validated fixed laboratory-based method (Touchscreen) and a newly developed clinically-relevant portable prototype (Stand-alone) yield comparable results in the assessment of psychoacoustic tinnitus pitch and loudness. Participants with tinnitus (N=15, 7 with normal hearing and 8 with hearing loss) and participants simulating tinnitus (Simulators, N=15) were instructed to rate the likeness of pure tones (250 – 16 kHz) to their tinnitus pitch and match their loudness using both methods presented in a counterbalanced order. Results indicate that simulators rated their “tinnitus” at lower frequencies and at louder at louder levels (~ 10 dB) compared to tinnitus participants. Tinnitus subgroups (with vs. without hearing loss) differed in their predominant tinnitus pitch (i.e., lower in the tinnitus with hearing loss subgroups) but not in their loudness matching in dB SL. Loudness at the predominant pitch was identified as a factor yielding significant sensitivity and specificity in discriminating between the two groups of participants. Importantly, despite differences in the devices’ physical presentations, likeness and loudness ratings were globally consistent between the two methods and, moreover, highly reproducible from one method to the other in both groups. All in all, both methods yielded robust tinnitus data in less than 12 minutes, with the Stand-alone having the advantage of not being dependent of learning effects, being user-friendly, and being adapted to the audiogram of each patient to further reduce testing time.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".