Abstract

Cannabis remains the most used illegal substance across the globe, and negative outcomes and disorders are common. A spotlight therefore falls on reductions in cannabis use in people with cannabis use disorder. Current estimates of unassisted cessation or reduction in cannabis use rely on community surveys, and few studies focus on individuals with disorder. A key interest of services and researchers is to estimate effect size of reductions in consumption among treatment seekers who do not obtain treatment. Effects within waiting list or information-only control conditions of randomised controlled trials offer an opportunity to study this question. This paper examines the extent of reductions in days of cannabis use in the control groups of randomised controlled trials on treatment of cannabis use disorders. A systematic literature search was performed to identify trials that reported days of cannabis use in the previous 30 (or equivalent). Since all but one of the eight identified studies had delayed treatment controls, results could only be summarised across 2–4 months. Average weighted days of use in the previous 30 days fell from 24.5 to 19.9, and a meta-analysis using a random effects model showed an average reduction of 0.442 SD. However, every study had at least one significant methodological issue. While further high-quality data is needed to confirm the observed effects, these results provide a baseline from which researchers and practitioners can estimate the extent of change required to detect effects of cannabis treatments in services or treatment trials.

Abstract

Public demand for information about health matters has led several organizations to develop custom systems for self-management of medical information. These systems facilitate the management of this information by the public, health care professionals, public institutions, companies and groups of patients. Thanks to new technologies, patients are taking an active role in searching the internet with queries about health-related issues, which gives rise to a debate about the accreditations of web pages that disseminate health information. In this context, Google, with applications such as Google Health, Flu Trends and Android, and Microsoft, with its HealthVault utility, have become the maximum exponents of medical information management software.

Abstract

MPLS DiffServ-TE presents the solution awaited so much by the network service providers by allowing a differentiation of services and a traffic engineering based on a fast packet switching technology. However, the management of such a network is not a simple function and could not be done manually. In this paper, we propose a novel architecture based on the Multi-Agent Systems (MAS) capable of managing automatically MPLS DiffServ-TE domains. Based on the network states, our intelligent agents take the appropriate decisions. They, for example, reconfigure the network accordingly. Document type: Part of book or chapter of book