White noise effect on listening effort among patients with chronic tinnitus and normal hearing thresholds
Bibliographic record
Abstract
OBJECTIVE: This study investigated the effects of WN on LE in subjects with chronic tinnitus and normal hearing thresholds. The study was a prospective, non-randomized, before-and-after, intra-participant intervention. METHODS: Twenty-five subjects performed the following tests: conventional and high-frequency audiometry, acuphenometry, screening questionnaires for depression and anxiety symptoms, Tinnitus Handicap Inventory (THI), Montreal Cognitive Assessment, and high WM test from the Working Memory Assessment Battery, Federal University of Minas Gerais (WMAB) as the LE measure in two conditions: No Added Noise (NAN) and with Added Noise (AN). RESULTS: Seventeen participants (68%) performed better on AN condition. Data analysis revealed a 45% improvement in the WMAB total span count on AN setting, with a significant p value (p=0.001). CONCLUSION: The subgroup of participants without traces of anxiety symptoms, up to mild traces of depressive symptoms, having unilateral tinnitus, and a THI level up to grade 2, had improved WM performance in the presence of WN, which suggests a release of cognitive resources and less auditory effort under these combined conditions. EVIDENCE LEVEL: 4.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".