Association between diabetes mellitus and tinnitus: A meta-analysis
Bibliographic record
Abstract
Diabetes mellitus (DM) has been suggested as a potential risk factor for tinnitus, but evidence remains inconclusive. This meta-analysis aimed to evaluate the association between DM and tinnitus by systematically reviewing and synthesizing data from observational studies. A comprehensive literature search was conducted in PubMed, Embase, and Web of Science up to August 16, 2024. Observational studies with a sample size of at least 100 participants that assessed the relationship between DM and tinnitus were included. Studies involving populations with specific diseases were excluded. Odds ratios (ORs) and 95% confidence intervals (CIs) were pooled using a random-effects model. Study quality was assessed using the Newcastle-Ottawa Scale (NOS), and sensitivity and subgroup analyses were performed. Publication bias was evaluated using funnel plots and Egger's regression test. Twelve studies comprising 2,277,719 participants were included. The pooled analysis revealed a significant association between DM and tinnitus (OR: 1.18, 95% CI: 1.06-1.31, P = 0.002), with moderate heterogeneity (I² = 51%). Sensitivity analyses confirmed the robustness of these findings. Subgroup analyses showed no significant differences by geographical region, mean age, sex distribution, tinnitus diagnosis method, or model used for association estimation. Publication bias was not detected (Egger's test P = 0.29). These findings suggest that DM is significantly associated with an increased risk of tinnitus. Further research is warranted to explore underlying mechanisms and causal relationships. Nonetheless, the results underscore the importance of monitoring tinnitus in patients with diabetes.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.055 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".