Benzodiazepine Use and Driving
Bibliographic record
Abstract
OBJECTIVE: The purpose of the present study was to examine the experimental and epidemiologic evidence linking benzodiazepine use to driving impairment. DATA SOURCES: We searched MEDLINE, PsycINFO, the Cochrane Collaboration, and EMBASE using the key terms ("benzodiazepines" OR "exp benzodiazepines") AND ("automobile driving" OR "accidents, traffic" OR "driving" OR "driver$") and limited the results to English citations from 1966 to August 5, 2005, with auto-updates for MEDLINE and PsycINFO to November 30, 2007. STUDY SELECTION AND DATA EXTRACTION: Experimental studies using driving simulators and on-road tests were sought, as were epidemiologic studies of a case-control or cohort design. Data were extracted by blinded raters and pooled using random-effects models. We excluded studies without control groups or without measures of driving or collisions. Studies with driving measures that could not be combined were also excluded. DATA SYNTHESIS: Of 405 potential articles, 11 epidemiologic and 16 experimental studies were included in the meta-analysis. Associations between motor vehicle collisions (MVCs) and benzodiazepine use were found among 6 case-control studies (OR = 1.61, 95% CI = 1.21 to 2.13, p <.001), and 3 cohort studies (OR = 1.60, 95% CI = 1.29 to 1.97, p <.0001). Only 10 of 97 experimental driving variables could be pooled for analysis. While no consistent findings were observed in studies using driving simulators, increased deviation of lateral position was found on on-road driving tests (standardized mean difference = 0.80, 95% CI = 0.35 to 1.25, p = .0004). CONCLUSION: Benzodiazepine users were found to be at a significantly increased risk of MVCs compared to nonusers, and these differences may be accounted for by a difficulty in maintaining road position.
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 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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".