A scoping review of motor vehicle operator performance assessments for benzodiazepine receptor agonists
Bibliographic record
Abstract
Benzodiazepines and Z -drugs (e.g., zopiclone, zolpidem) (benzodiazepine receptor agonists or BZRAs), are prescribed for anxiety and insomnia disorders. However, they are not indicated as first line therapies for long-term management due to harms and efficacy limitations. BZRAs have also been associated with traffic accident risks. Patients taking BZRAs are told to consult with health care providers regarding motor vehicle operation safety. However, advice on driving is variable. The objective of this scoping review is to identify, map, and characterize the evidence for assessments that measure driving performance in people taking BZRAs. Embase (Elsevier), MEDLINE (Ovid), and PsycINFO (EBSCO) were searched. Covidence was used for screening. Each stage of screening included two independent reviewers. A REDCap database was used for data extraction by two independent reviewers. Results were tabulated and summarised as a narrative. Driving performance was assessed with 20 unique BZRAs across 183 studies ( n = 92 experimental; n = 91 observational) in 178 publications. Zopiclone was the most studied. In experimental studies, the Standard Deviation of Lateral Position (SDLP) was used most often ( n = 54, 62 %) and many studies ( n = 35, 38 %) were conducted in the Netherlands. For observational studies, biological detection (e.g., urine, blood) ( n = 73, 80 %) followed by prescription drug/dispensing records ( n = 17, 19 %) were the most common impairment measures and Norway ( n = 20) is where most studies took place. In experimental studies, most ( n = 89, 97 %) were conducted using only one driving setting. Simulated driving in a car ( n = 36) and road driving in traffic (n = 36) were common as compared to nontraffic driving course (n = 8) and simulated driving ( n = 9). In experimental studies, seventy-eight of the 92 studies (85 %) had at least one measure that identified impairment. BZRA effects on motor vehicle driving performance have been studied using heterogenous protocols with multiple measures and settings, ranging from simulation to authentic traffic situations in experimental studies to biological detection and dispensing records in observational studies. Many BZRAs have been studied but study representation does not match prescribing pattern prevalence. The interpretation and contextualization of results for clinical practice is challenging due to the complexity (i.e., protocols, measures, settings). Future work in this area should work to improve knowledge translation of results so information is more readily accessible and applicable to health care providers and patients.
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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 | 0.017 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".