Depression in Patients with Tinnitus: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Tinnitus is a condition that causes distress and impairment across cognitive, functional, and psychiatric spectra. In the psychiatric realm, tinnitus has long been associated with depression. To better characterize the co-occurrence of depression and tinnitus, we performed a systematic review of the prevalence of depression among patients with tinnitus. DATA SOURCES: We comprehensively examined original studies reporting the prevalence of depression in adult populations with tinnitus, as indexed in the PubMed and Web of Science databases and published from January 2006 to August 2016. REVIEW METHODS: All identified articles were reviewed independently by 2 researchers, with a third reviewer for adjudication. Included studies were evaluated for threats to validity across 3 domains-representativeness, response rate, and ascertainment of outcome-on a 4-point modified Newcastle-Ottawa Quality Assessment Scale. RESULTS: Twenty-eight studies were included, representing 15 countries and 9979 patients with tinnitus. Among the included studies, the median prevalence of depression was 33%, with an interquartile range of 19% to 49% and an overall range of 6% to 84%. Studies were high quality overall, with a mean score of 3.3 (SD = 0.76), and 89% utilized a validated tool to ascertain depression. CONCLUSIONS: We conducted one of the largest contemporary comprehensive reviews, which suggests a 33% prevalence of depression among patients with tinnitus. Our review reaffirms that a substantial proportion of patients with tinnitus have depression, and we recommend that all who treat tinnitus should screen and treat their patients for depression, if present.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".