Driving performance assessments for benzodiazepine receptor agonist–related impairment: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this scoping review is to identify, map, and characterize the evidence for assessments that measure driving performance in people taking benzodiazepine receptor agonists. INTRODUCTION: Benzodiazepines and Z-drugs are widely prescribed for the treatment of anxiety disorders and insomnia even though they are not recommended as an initial treatment for these indications. Benzodiazepine and Z-drug use is associated with an elevated risk of traffic accidents, and guidance documents instruct patients to consult with their health care providers for instructions on how to safely operate a motor vehicle while consuming these medications. However, little is known about the assessments that measure driving performance regarding the extent and length of impairment from the consumption of the individual benzodiazepines and Z-drugs. INCLUSION CRITERIA: Eligible studies will include participants who are new, intermittent, or chronic users of benzodiazepines and Z-drugs. No exclusions will be applied regarding the health status of participants or whether their benzodiazepine and Z-drug use is for an approved indication as indicated by government agencies (eg, Health Canada) or practice guidelines. Studies that examine the consumption of a benzodiazepine and Z-drug in association with the operation of a motor vehicle (real or simulated) with direct or indirect objective or standard subjective measures or indicators of impairment while operating a motor vehicle will be considered. METHODS: Embase (Elsevier), MEDLINE (Ovid), and PsycINFO (EBSCO) will be searched as sources of published studies. Only studies published in English will be included, and there will be no limit on dates of publication. After screening the titles and abstracts of identified citations, two independent reviewers will retrieve potentially relevant full-text studies and extract data. Data will be presented in diagrammatic or tabular form accompanied by a narrative summary.
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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 | 0.064 | 0.059 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.054 | 0.010 |
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".