Addiction
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Randomized Controlled Trial Comparative Study
Vaping characteristics and expectancies are associated with smoking cessation propensity among dual users of combustible and electronic cigarettes.
Most e-cigarette users who also smoke combustible cigarettes (dual users) begin vaping to quit smoking, yet only a subset succeeds. We hypothesized that reinforcing characteristics of e-cigarettes (vaping reinforcement) would positively predict smoking cessation propensity (SCP) among dual users. ⋯ Among e-cigarette users who also smoke combustible cigarettes, frequent vaping combined with positive e-cigarette expectancies appears to predict greater smoking cessation propensity. However, vaping enthusiasm (measured by e-cigarette modifications, using non-tobacco flavors and puffs per use), higher nicotine content and use of tobacco flavored solution may reduce cessation propensity.
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Randomized Controlled Trial Comparative Study
Pharmacokinetics of a novel, approved, 1.4-mg intranasal naloxone formulation for reversal of opioid overdose-a randomized controlled trial.
Intranasal (i.n.) naloxone is an established treatment for opioid overdose. Anyone likely to witness an overdose should have access to the antidote. We aimed to determine whether an i.n. formulation delivering 1.4 mg naloxone hydrochloride would achieve systemic exposure comparable to that of 0.8 mg intramuscular (i.m.) naloxone. ⋯ Intranasal 1.4 mg naloxone provides adequate systemic concentrations to treat opioid overdose compared with intramuscular 0.8 mg, without statistical difference on maximum plasma concentration, time to maximum plasma concentration or area under the curve. Simulations support its appropriateness both as peer administered antidote and for titration of treatment by professionals.
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E-cigarettes (EC) and nicotine replacement therapy (NRT) are less harmful than smoking, but misperceptions of relative harm are common. Aims were to (1) assess nicotine knowledge and perceptions of: harm of EC and NRT relative to smoking, addictiveness of EC relative to smoking, and change in harm to user if smoking replaced with EC; (2) define associations of these perceptions with respondent characteristics including nicotine knowledge; and (3) explore perceived main harms of EC and whether these differ by vaping status. ⋯ Large proportions of UK smokers and ex-smokers overestimate the relative harmfulness of e-cigarettes and nicotine replacement therapy compared with smoking; misattributing smoking harms to nicotine is associated with increased misperceptions.
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Comparative Study
Machine-learning prediction of adolescent alcohol use: a cross-study, cross-cultural validation.
The experience of alcohol use among adolescents is complex, with international differences in age of purchase and individual differences in consumption and consequences. This latter underlines the importance of prediction modeling of adolescent alcohol use. The current study (a) compared the performance of seven machine-learning algorithms to predict different levels of alcohol use in mid-adolescence and (b) used a cross-cultural cross-study scheme in the training-validation-test process to display the predictive power of the best performing machine-learning algorithm. ⋯ Computerized screening software shows promise in predicting the risk of alcohol use among adolescents.
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The World Health Organization's (WHO's) proposed International Classification of Diseases, 11th edition (ICD-11) includes several major revisions to substance use disorder (SUD) diagnoses. It is essential to ensure the consistency of within-subject diagnostic findings throughout countries, languages and cultures. To date, agreement analyses between different SUD diagnostic systems have largely been based in high-income countries and clinical samples rather than general population samples. We aimed to evaluate the prevalence of, and concordance between diagnoses using the ICD-11, The WHO's ICD 10th edition (ICD-10) and the Diagnostic and Statistical Manual of Mental Disorders, 4th and 5th editions (DSM-IV, DSM-5); the prevalence of disaggregated ICD-10 and ICD-11 symptoms; and variation in clinical features across diagnostic groups. ⋯ The World Health Organization's proposed International Classification of Diseases, 11th edition (ICD-11) classifications for substance use disorder diagnoses are highly consistent with the ICD 10th edition and the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV). Concordance between ICD-11 and the DSM 5th edition (DSM-5) varies, due largely to low levels of agreement for the ICD harmful use and DSM-5 mild use disorder. Diagnostic validity of self-reported 'harm to others' is questionable.