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Marijuana is the most widely consumed illicit substance in the United States, and an increasing number of states have legalized it for both medicinal and recreational purposes. As it becomes more readily available, there will be a concurrent rise in the number of users and, consequently, the number of motor vehicle operators driving under the influence. This article examines the cognitive and psychomotor effects of cannabis, as well as current policy concerning driving under the influence of drugs. The authors performed a MEDLINE search on the epidemiology of cannabis use, its cognitive and psychomotor effects, and policies regarding driving under the influence of drugs. Twenty-eight epidemiological studies, 16 acute cognitive and psychomotor studies, 8 chronic cognitive and psychomotor studies, and pertinent state and federal laws and policies were reviewed. These search results revealed that marijuana use is associated with significant cognitive and psychomotor effects. In addition, the legalization of marijuana varies from state to state, as do the laws pertaining to driving under the influence of drugs. Marijuana is a commonly found illicit substance in motor vehicle operators driving under the influence of drugs. Current evidence shows that blood levels of tetrahydrocannabinol do not correlate well with the level of impairment. In addition, although acute infrequent use of cannabis typically leads to cognitive and psychomotor impairment, this is not consistently the case for chronic heavy use. To establish the framework for driving under the influence of cannabis policy, we must review the current published evidence and examine existing policy at state and federal levels.

Background: Low back pain is the leading cause of years lost to disability with approximately 15%-25% of the chronic back pain population suffering from lumbar facet arthropathy. No large-scale study has sought to systematically identify inciting events for lumbar facet arthropathy. The aim of this study is to quantify the proportion of individuals with lumbar facetogenic pain who report a specific precipitating event(s) and to determine if there is a correlation between these events and treatment outcome., Methods: Institutional electronic medical records were searched based on the current procedural terminology (CPT) codes representing lumbar facet joint radiofrequency ablation for procedures performed between January 2007 and December 2015. All patients had obtained ≥50% pain relief based on 6-hour pain diaries after 1 or more diagnostic facet blocks. A positive outcome was defined as ≥50% pain relief sustained for longer than 3-month after procedure, without additional procedural interventions., Results: One thousand sixty-nine people were included in analysis. In the 52% of individuals who described an inciting event, the most commonly reported causes were falls (11%), motor vehicle collisions (11%), sports-related injuries (11%, of which weightlifting accounted for 62%), nonspine postsurgical injuries (2%), and "other" (17%). Six hundred seventeen (57.7%) individuals experienced ≥50% pain relief sustained for >3 months. Patients whose pain was preceded by an inciting event were more likely to have a positive outcome than those who could not recall a specific precipitating factor (odds ratio, 1.5; confidence interval, 1.02-2.1, P = .01). Another factor associated with outcome was shorter duration of pain (8.1 ± 9.2 vs 9.7 ± 10.1 years, P = .02), with an observed modifier effect of age on outcomes. For a 1-year increase in age, there was a 10% increase in the odds of a positive response., Conclusions: Inciting events are common in patients diagnosed with lumbar facetogenic pain and may be associated with a positive outcome.

Background: Interventional pain treatment centers represent an integral part of interdisciplinary care. Barriers to effective treatment include access to care and financial issues related to pain clinic operations. To address these challenges, specialty clinics have taken steps to identify and remedy missed clinic appointments. However, no prospective study has sought to identify factors associated with pain clinic "no-shows.", Methods: We performed a prospective, longitudinal year-long study in an inner-city, academic pain clinic in which patients scheduled for office visits and procedures were categorized as to whether they showed up or did not show up for their scheduled appointment without cancelling the day before. Twenty demographic (age, employment status), clinical (eg, diagnosis, duration of pain), and environmental (season, time and day of appointment) variables were assessed for their association with missing an appointment. The logistic regression model predicting no-shows was internally validated with crossvalidation and bootstrapping methods. A predictive nomogram was developed to display effect size of predictors for no-shows., Results: No-show data were collected on 5134 patients out of 5209 total appointments for a capture rate of 98.6%. The overall no-show rate was 24.6% and was higher in individuals who were young (<65 years), single, of ethnic minority background, received Medicare/Medicaid, had a primary diagnosis of low back pain or headaches, were seen on a day with rain or snow or for an initial consult, and had at least 1 previous pain provider. Model discrimination (area under curve) was 0.738 (99% confidence interval, 0.70-0.85). A minimum threshold of 350 points on the nomogram predicted greater than 55% risk of no-shows., Conclusions: We found a high no-show rate, which was associated with predictable and unpredictable (eg, snow) factors. Steps to reduce the no-show rate are discussed. To maximize access to care, operation managers should consider a regression model that accounts for patient-level risk of predictable no-shows. Knowing the patient level, no-show rate can potentially help to optimize the schedule programming by staggering low- versus high-probability no-shows.