Tinnitus risk factors and its evolution over time: a cohort study
Bibliographic record
Abstract
Abstract Background Subjective tinnitus is an auditory percept unrelated to an external sound source. The lack of curative treatments and limited understanding of its risk factors complicate the prevention and management of this distressing symptom. This study seeks to identify socio-demographic, psychological, and health-related risk factors predicting tinnitus presence (how often individuals perceive tinnitus) and severity separately, and their evolution over time. Methods Using the UK Biobank dataset which encompasses data on the socio-demographic, physical, mental and hearing health from more than 170,000 participants, we trained two distinct machine learning models to identify risk scores predicting tinnitus presence and severity separately. These models were used to predict tinnitus over time and were replicated in 463 individuals from the Tinnitus Research Initiative database. Finding Machine learning based approach identified hearing health as a primary risk factor for the presence and severity of tinnitus, while mood, neuroticism, hearing health, and sleep only predicted tinnitus severity. Only the severity model accurately predicted the evolution over nine years, with a large effect size for individuals developing severe tinnitus (Cohen’s d = 1.10, AUC-ROC = 0.70). To facilitate its clinical applications, we simplified the severity model and validated a five-item questionnaire to detect individuals at risk of developing severe tinnitus. Interpretation This study is the first to clearly identify risk factors predicting tinnitus presence and severity separately. Hearing health emerges as a major predictor of tinnitus presence, while mental health plays a crucial role in its severity. The successful prediction of the evolution of tinnitus severity over nine years based on socio-emotional, hearing and sleep factors suggests that modifying these factors could mitigate the impact of tinnitus. The newly developed questionnaire represents a significant advancement in identifying individuals at risk of severe tinnitus, for which early supportive care would be crucial. Funding Horizon Europe Marie Slodowska-Curie Actions, the Fondation des gueules cassées, the Fondation pour l’Audition, the Louise and Alan Edwards Foundation, the Canadian Institutes Health Research, the Institut TransMedTech and the Canada First Research Excellence Fund.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".