Search

Summary Chemsex occurs primarily among gay, bisexual and other men who have sex with men (GBMSM), and there is evidence of a subgroup of users who carry out chemsex-related criminal offences and experience harm. Challenges with chemsex can present to various settings; there are concerns that harm is increasing, including at interfaces between health, social care and criminal justice systems. The UK response to date has lacked a coordinated approach. An expert reference group was convened to share chemsex knowledge, articulate priorities for research and pathway development, and foster collaborative working between agencies. It made three key recommendations: develop and increase training and awareness across all services; implement a coordinated research programme with the development of a common data-set and assessment tool to fully characterise population-level needs; develop a professional network to share information, provide professional support and act as a knowledge hub. There was support for a unified multi-agency strategy incorporating the priorities identified as overarching principles.

Colleen A Burke,1,2,&ast; Kenneth A Taylor,1,3,&ast; Rebecca Fillipo,2 Steven Z George,1– 3 Flavia P Kapos,1,3 Stephanie T Danyluk,1 Carla A Kingsbury,4 Kelley Seebeck,1 Christopher E Lewis,4 Emily Ford,4 Cecilia Plez,4 Andrzej S Kosinski,5 Michael C Brown,6,7 Adam P Goode1– 3 1Department of Orthopedic Surgery, Duke University School of Medicine, Durham, NC, USA; 2Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA; 3Duke Clinical Research Institute, Duke University, Durham, NC, USA; 4Duke Clinical and Translational Science Institute, Duke University School of Medicine, Durham, NC, USA; 5Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA; 6Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA; 7Duke Cancer Institute, Duke University, Durham, NC, USA&ast;These authors contributed equally to this workCorrespondence: Adam P Goode, Department of Orthopedic Surgery, Duke University School of Medicine, 311 Trent Drive, Durham, NC, 27710, USA, Tel +1 919 681 6157, Fax +1 919 684 1846, Email adam.goode@duke.eduIntroduction: Acute low back pain (LBP) is a common experience; however, the associated pain severity, pain frequency, and characteristics of individuals with acute LBP in community settings have yet to be well understood. In this manuscript, two acute-LBP severity categorization definitions were developed: 1) pain impact frequency (impact-based) and 2) pain intensity (intensity-based) severity categories. The purpose of this manuscript is to describe and then compare these acute-LBP severity groups in the following characteristics: 1) sociodemographic, 2) general and physical health, and 3) psychological using a feasibility cohort study.Methods: This cross-sectional study used baseline data from 131 community-based participants with acute LBP (< 4 weeks duration before screening and ≥ 30 pain-free days before acute LBP onset). Descriptive associations were calculated as prevalence ratios of categorical variables and Hedges’ g for continuous variables.Results: Our analyses identified several large associations for impact-based and intensity-based categories with global mental health, global physical health, STarT Back Screening Tool risk category, and general health. Larger associations were found with social constructs (racially and ethnically minoritized, performance of social roles, and isolation) when using the intensity-based versus impact-based categorization.Discussion: This study adds to the literature by providing standard ways to characterize community-based individuals experiencing acute-LBP. The robust differences observed between these categorization approaches suggest that how we define acute-LBP severity is consequential; these different approaches may be used to improve the early identification of factors potentially contributing to the development of chronic-LBP.Keywords: low back pain, acute pain, cohort study, community

Opinion summarization and sentiment classification are key processes for understanding, analyzing, and leveraging information from customer opinions. The rapid and ceaseless increase in big data of reviews on e-commerce platforms, social media, or review portals becomes a stimulus for the automation of these processes. In recent years, deep transfer learning has opted to solve many challenging tasks in Natural Language Processing (NLP) relieving the hassles of exhaustive training and the requirement of extensive labelled datasets. In this work, we propose frameworks for Abstractive Summarization (ABS) and Sentiment Analysis (SA) of airline reviews using Pretrained Language Models (PLM). The abstractive summarization model goes through two finetuning stages, the first one, for domain adaptation and the second one, for final task learning. Several studies in the literature empirically demonstrate that review rating has a positive correlation with sentiment valence. For the sentiment classification framework, we used the rating value as a signal to determine the review sentiment, and the model is built on top of BERT (Bidirectional Encoder Representations from Transformers) architecture. We evaluated our models comprehensively with multiple metrics. Our results indicate competitive performance of the models in terms of most of the evaluation metrics.

Nina Hein,1 Conner Callaway,2 Devin Ford,2 John C Carlson1 1Ochsner Health System, Department of Allergy and Clinical Immunology, New Orleans, LA, USA; 2Tulane University, School of Medicine, New Orleans, LA, USACorrespondence: John C Carlson, Department of Pediatrics, Ochsner Health System, 1315 Jefferson Hwy, New Orleans, LA, 20121, USA, Tel +1-504-842-3900, Fax +1-504-842-5848, Email john.carlson@ochsner.org