Systematic Review of the Quality and Generalizability of Studies on the Effects of Opioids on Driving and Cognitive/Psychomotor Performance
Bibliographic record
Abstract
INTRODUCTION: The effect of opioids on driving performance has been much debated. Driving is a complex task requiring integration of psychomotor, cognitive, motor and decision-making skills, visual-spatial abilities, divided attention, and behavioral and emotional control. The objective of this systematic review was to assess the quality of studies and to revisit the concept that patients on stable opioids are safe to drive as it applies to everyday practice. METHODS: We searched MEDLINE, EMBASE, PSYCinfo, CENTRAL, TRANSPORT, CINAHL, reference lists of retrieved articles and narrative reviews, for studies on chronic cancer and noncancer pain patients on opioids, tested by driving, driving simulator, or cognitive/psychomotor tests. Methodological quality was assessed with Methodological Index for Nonrandomized Studies, cognitive/psychomotor tests were appraised regarding their sensitivity and validation, and whether confounding variables potentially affecting the study conclusions were recorded. The results were analyzed both quantitatively and qualitatively. RESULTS: We included 35 studies (2044 patients, 1994 controls), 9% of the studies were of poor, 54% of fair, and 37% of high quality; 3 quarters of the studies used high sensitivity cognitive tests. Amount and dose of opioids varied largely in many studies. Mean number of possible but unreported confounders was 2.2 (range, 0 to 4), relating to failure of the studies to mention co-prescriptions with psychotropic effects, pain severity, sleep disorder or daytime somnolence, and/or significant depressive or anxiety-related problems. INTERPRETATION: The commonly held concept that "chronic pain patients on stable opioids are safe to drive" cannot be generalized to all such patients in everyday practice, but may be applicable only to a subset who meet certain criteria.
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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 | 0.076 | 0.285 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.013 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".