Cognitive Screening and Hearing Assessment in Patients With Chronic Tinnitus
Bibliographic record
Abstract
OBJECTIVES: The study aimed to assess the relationship of tinnitus with hyperacusis with cognitive impairment as indicated by the Montreal Cognitive Assessment (MoCA) tool. METHODS: This multicenter cross-sectional study included individuals with chronic tinnitus from the "Unification of Treatments and Interventions for Tinnitus Patients" (UNITI) database. Participants were recruited from four different tertiary clinical centers located in Athens and Granada (Mediterranean group), as well as Berlin and Regensburg (German group). In total, 380 individuals with a diagnosis of non-pulsatile chronic tinnitus (permanent and constant tinnitus lasting more than 6 months) and no evidence of severe cognitive impairment (MoCA score >22) were enrolled. The evaluation utilized the following tools: MoCA, Tinnitus Handicap Inventory (THI), Hyperacusis Questionnaire (GÜF), Patient Health Questionnaire (PHQ-9), and the European School for Interdisciplinary Tinnitus Research Screening Questionnaire. RESULTS: MoCA scores differed between German and Mediterranean individuals (P<0.01), necessitating separate analyses for each group. In both cohorts, MoCA scores were significantly associated with education level, age, hearing threshold at 8 kHz, and THI. Furthermore, a significant correlation was observed between PHQ-9 scores and both THI and GÜF (P<0.01 for both Germans and those from the Mediterranean). CONCLUSION: Our data suggest an association between tinnitus handicap, high-frequency hearing loss, and mild cognitive impairment. Additionally, PHQ-9 scores were associated with tinnitus and hyperacusis scores, independent of hearing loss thresholds.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".