Occupational Injuries and Use of Benzodiazepines: A Systematic Review and Metanalysis
Bibliographic record
Abstract
Background: Benzodiazepines have been widely used in clinical practice for over four decades and continue to be one of the most consumed and highly prescribed class of drugs available in the treatment of anxiety, depression, and insomnia. The literature indicates that Benzodiazepine users at a significantly increased risk of Motor Vehicle accidents compared to non-users but the impact on injuries at workplace is not well-defined. We aimed to investigate whether use of benzodiazepine is associated with increased risk of occupational injuries (OI). Methods: PubMed, Embase, and Scopus databases were searched. A meta-analysis was performed to calculate odds ratio (OR) and 95% confidence interval (CI) among case controls, cross-sectional studies, either questionnaire or laboratory exams based. Results: A total of 13 studies met inclusion criteria, involving 324,168 OI from seven different countries, with an estimated occurrence of benzodiazepine positivity of 2.71% (95% CI 1.45–4.98). A total of 14 estimates were retrieved. Of them, 10 were based on laboratory analyses, three on institutional databases, while one study was based on questionnaires. Regarding the occupational groups, three estimates focused on commercial drivers (0.73%, 95% CI 0.12–4.30), that exhibited a reduced risk ratio for benzodiazepine positivity compared to other occupational groups (RR 0.109, 95% CI 0.063–0.187). Eventually, no increased risk for benzodiazepine positivity was identified, either from case control studies (OR 1.520, 95% CI 0.801–2.885, I2 76%), or cross sectional studies, when only laboratory based estimates were taken in account (OR 0.590, 95% CI 0.253–1.377, I2 63%). Conclusions: Even though benzodiazepines have the potential to increase injury rates among casual and chronic users, available evidence are insufficient to sustain this hypothesis, particularly when focusing on laboratory-based studies (i.e., studies the characterized the benzodiazepine immediately before the event).
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.042 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".