An Exploratory Study of the Effect of Tinnitus on Listening Effort Using EEG and Pupillometry
Bibliographic record
Abstract
OBJECTIVE: Previous behavioral studies on listening effort in tinnitus patients did not consider extended high-frequency hearing thresholds and had conflicting results. This inconsistency may be related that listening effort is not evaluated by the central nervous system (CNS) and autonomic nervous system (ANS), which are directly related to tinnitus pathophysiology. This study matches hearing thresholds at all frequencies, including the extended high-frequency and reduces hearing loss to objectively evaluate listening effort over the CNS and ANS simultaneously in tinnitus patients. STUDY DESIGN: Case-control study. SETTING: University hospital. METHODS: Sixteen chronic tinnitus patients and 23 matched healthy controls having normal pure-tone averages with symmetrical hearing thresholds were included. Subjects were evaluated with 0.125 to 20 kHz pure-tone audiometry, Montreal Cognitive Assessment Test (MoCA), Tinnitus Handicap Inventory (THI), Visual Analog Scale (VAS), electroencephalography (EEG), and pupillometry. RESULTS: Pupil dilation and EEG alpha band in the "coding" phase of the sentence presented in tinnitus patients was less than in the control group (p < .05). VAS score was higher in the tinnitus group (p < .01). Also, there was no statistically significant relationship between EEG and pupillometry components and THI or MoCA (p > .05). CONCLUSION: This study suggests that tinnitus patients may need to make an extra effort to listen. Also, pupillometry may not be sufficiently reliable to assess listening effort in ANS-related pathologies. Considering the possible listening difficulties in tinnitus patients, reducing the listening difficulties, especially in noisy environments, can be added to the goals of tinnitus therapy protocols.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".