Investigating the Role of Working Patterns in Tinnitus: Results From a Large UK Population
Bibliographic record
Abstract
OBJECTIVES: This study aimed to examine the association of different working patterns and tinnitus. DESIGN: This cross-sectional study (2006-2010, n = 91,089) was a secondary analysis of existing data from the UK Biobank. It exploratorily evaluated the association between various working patterns, including shift work (day workers/sometimes/frequent), night shift work (day workers/rarely/sometimes/frequent), heavy work (never/sometimes/usually/always), work satisfaction (very happy/moderately happy/moderately unhappy/very unhappy), standing work (never/sometimes/usually/always) and workplace noise (no/exposing <1 year/1 to 5 years/>5 years) and the occurrence (yes/no), frequency (constant/transient) and severity (troublesome/not troublesome) of tinnitus. Univariate and multivariable logistic regression analysis models were conducted. Sub-analysis was performed to estimate the effects of age, sex, and different working factors on tinnitus. RESULTS: The study results showed that occasional shift and night shift were associated with an increased risk of tinnitus, while frequent shift/night shift showed no such association. This risk was further exacerbated by increased heavy work, prolonged standing work, lower job satisfaction, and extended exposure to noisy workplaces. Specifically, being occasionally engaged in shift/night shift, increasing workload, and short-term noise exposure (<1 year) were correlated with "transient tinnitus," while long-time noise exposure (>5 years) was identified as a significant risk factor for "constant tinnitus." Lower work satisfaction and noise exposure for more than 1 year were positively associated with "troublesome tinnitus." CONCLUSIONS: Irregular working shifts, increasing physical workload, lower work satisfaction, and longer noise exposure were related to the occurrence, frequency, and severity of tinnitus in the UK Biobank cohort. Therefore, targeted interventions aimed at improving working patterns may help prevent tinnitus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".