Reliability of some tinnitus psychoacoustic measures
Bibliographic record
Abstract
Introduction Psychoacoustic measures are used to quantify tinnitus perception in clinical and research settings. Residual inhibition (RI), for instance, is defined as a temporary reduction in tinnitus loudness after the presentation of an acoustic stimulus. A novel approach was developed recently to assess the minimum sound level producing RI, the minimum residual inhibition level (MRIL), after the repeated presentation of short sounds separated by short periods of silence. Two studies were performed: the objective of the study 1 was to assess the reliability and the reproducibility of this measures as well as others, including the minimum masking level (MML). The objective of study 2 was to assess whether a smoother envelope of the sound would produce similar levels of MML and MRIL and improve listening comfort. Methods Thirty participants with chronic tinnitus completed each study. For study 1, MML and MRIL were obtained at the seven standard audiometric frequencies at around 1-month interval. For study 2, MML and MRIL were obtained using triangular and trapezoidal-shaped NBN signals centered at three different frequencies. Results The mean absolute test-retest difference was 3 dB for both MML and MRIL [Intraclass correlation, ICC (2,1) of 0.70 and 0.84, respectively] for the measures obtained in test and retest (86% of cases for MML and 21% of cases for complete MRIL). While the two types of envelopes provided comparable MRIL, the “triangular” envelope provided more listening comfort. Discussion In sum, the novel approach provides quick and reliable MML and MRIL measurement, with the potential benefit of subtyping participants.
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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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".