The Impact of Mobile Phone Use on Tinnitus: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Tinnitus is a perception of sound in the absence of an external source. The aim of our study was to investigate with a meta-analytical approach, whether mobile phone (MP) use increases the risk of tinnitus. Eight studies reporting the risk of tinnitus in relation to MP use were identified, and six high-quality studies (two cohort studies, one case-control study, and three cross-sectional ones) were included in the meta-analysis. The quality assessment was performed using the Newcastle-Ottawa scale. The risk of tinnitus was analyzed depending on the exposure to MPs in subgroups according to the study design and method of exposure assessment. Two cohort studies, which assessed the exposure to MPs using network operator data, indicated no significantly increased risk of tinnitus among highly exposed MP users in comparison to lightly exposed individuals (odds ratio [OR]: 1.03 [95% confidence interval [CI]: 0.93-1.15]). Likewise, the self-reported exposure data from two cohorts/case-control and four cross-sectional studies did not find an association between exposure to MPs and tinnitus (OR: 1.20 [95% CI: 0.40-3.61] and OR: 1.73 [95% CI: 0.67-4.49], respectively). Current scientific knowledge, including high-quality studies with a reliable exposure assessment based on network operator data, does not support the hypothesis that MP use is associated with tinnitus. © 2020 Bioelectromagnetics Society.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".