Tinnitus Is Associated with a Higher Risk of Benign Brain Tumors: A Nationwide, Population-Based Secondary Cohort Study of Young and Middle-Aged Adults
Bibliographic record
Abstract
BACKGROUND: It remains unclear whether tinnitus is associated with a higher risk of benign or malignant brain tumors in humans. Therefore, the aim of this secondary study was to investigate the risk of brain tumors in adult with tinnitus using data from a nationwide health claims research database. METHODS: Patients aged 20-50 years who were newly diagnosed with tinnitus were identified from the Taiwan's National Health Insurance Research Database and they served as the study cohort. A comparison cohort was formed by using patients without tinnitus from the same database with frequency matching (4: 1) by 10-year age interval and gender to the patients in the tinnitus cohort. Cox proportional hazards models were used to calculate the adjusted hazard ratios (AHR) for benign and malignant brain tumors in patients with tinnitus, adjusting for age, gender, and comorbidities. RESULTS: There were 15,819 patients in the tinnitus cohort and 63,276 in the comparison cohort. A significantly higher proportion of patients with tinnitus had benign brain tumor (p = 0.003) and all 11 comorbid conditions (p < 0.001) compared to those without tinnitus. Cox proportional hazards regression analysis performed on the basis of age, gender, and the 11 comorbidities revealed that tinnitus was independently associated with a higher risk for benign brain tumor (AHR 1.65, 95% CI 1.24-2.20, p = 0.001) and but not with malignant brain tumors (AHR 1.66, 95% CI 0.93-2.94, p = 0.085). CONCLUSIONS: Findings from this secondary cohort analysis indicated that tinnitus is associated with a higher risk of benign brain tumors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".