A new statistical model for binge drinking pattern classification in college-student populations

Background

Binge drinking (BD) among students is a frequent alcohol consumption pattern that produces adverse consequences. A widely discussed difficulty in the scientific community is defining and characterizing BD patterns. This study aimed to find homogenous drinking groups and then provide a new tool, based on a model that includes several key factors of BD, to assess the severity of BD regardless of the individual's gender.

Methods

Using the learning sample (N1 = 1,271), a K-means clustering algorithm and a partial proportional odds model (PPOM) were used to isolate drinking and behavioral key factors, create homogenous groups of drinkers, and estimate the probability of belonging to these groups. Robustness of our findings were evaluated with Two validations samples (N2 = 2,310, N3 = 120) of French university students (aged 18-25 years) were anonymously investigated via demographic and alcohol consumption questionnaires (AUDIT, AUQ, Alcohol Purchase Task for behavioral economic indices).

Results

The K-means revealed four homogeneous groups, based on drinking profiles: low-risk, hazardous, binge, and high-intensity BD. The PPOM generated the probability of each participant, self-identified as either male or female, to belong to one of these groups. Our results were confirmed in two validation samples, and we observed differences between the 4 drinking groups in terms of consumption consequences and behavioral economic demand indices.

Conclusion

Our model reveals a progressive severity in the drinking pattern and its consequences and may better characterize binge drinking among university student samples. This model provides a new tool for assessing the severity of binge drinking and illustrates that frequency of drinking behavior and particularly drunkenness are central features of a binge drinking model.

Glioblastoma Molecular Classification Tool Based on mRNA Analysis: From Wet-Lab to Subtype

Most glioblastoma studies incorporate the layer of tumor molecular subtype based on the four-subtype classification system proposed in 2010. Nevertheless, there is no universally recognized and convenient tool for glioblastoma molecular subtyping, and each study applies a different set of markers and/or approaches that cause inconsistencies in data comparability and reproducibility between studies. Thus, this study aimed to create an applicable user-friendly tool for glioblastoma classification, with high accuracy, while using a significantly smaller number of variables. The study incorporated a TCGA microarray, sequencing datasets, and an independent cohort of 56 glioblastomas (LUHS cohort). The models were constructed by applying the Agilent G4502 dataset, and they were tested using the Affymetrix HG-U133a and Illumina Hiseq cohorts, as well as the LUHS cases. Two classification models were constructed by applying a logistic regression classification algorithm, based on the mRNA levels of twenty selected genes. The classifiers were translated to a RT-qPCR assay and validated in an independent cohort of 56 glioblastomas. The classification accuracy of the 20-gene and 5-gene classifiers varied between 90.7-91% and 85.9-87.7%, respectively. With this work, we propose a cost-efficient three-class (classical, mesenchymal, and proneural) tool for glioblastoma molecular classification based on the mRNA analysis of only 5-20 genes, and we provide the basic information for classification performance starting from the wet-lab stage. We hope that the proposed classification tool will enable data comparability between different research groups.

Examining the nature of interprofessional practice: An initial framework validation and creation of the InterProfessional Activity Classification Tool (InterPACT)

The practice of, and research on interprofessional working in healthcare, commonly referred to as teamwork, has been growing rapidly. This has attracted international policy support flowing from the growing belief that patient safety and quality of care can only be achieved through the collective effort of the multiple professionals caring for a given patient. Despite the increasing policy support, the evidence for effectiveness lags behind: while there are supporting analytic epidemiological studies, few reliable intervention studies have been published and so we have yet to confirm a causal link. We argue that this lag in evidence development may be because interprofessional terms (e.g. teamwork, collaboration) remain conceptually unclear, with no common terminology or definitions, making it difficult to distinguish interventions from each other. In this paper, we examine published studies from the last decade in order to elicit current usage of terms related to interprofessional working; and, in so doing, undertake an initial empirical validation of an existing conceptual framework by mapping its four categories (teamwork, collaboration, coordination and networking) against the descriptions of interprofessional interventions in the included studies. We searched Medline and Embase for papers describing interprofessional interventions using a standard approach. We independently screened papers and classified these under set categories following a thematic approach. Disagreements were resolved through consensus. Twenty papers met our inclusion criteria. Identified interprofessional work interventions fall into a range, from looser to tighter links between members. Definitions are inconsistently and inadequately applied. We found the framework to be a helpful and practical tool for classifying such interventions more consistently. Our analysis enabled us to scrutinise the original dimensions of the framework, confirm their usefulness and consistency, and reveal new sub-categories. We propose a slightly revised typology and a classification tool (InterPACT) for future validation, with four mutually exclusive categories: teamwork, collaboration, coordination and networking. Consistent use, further examination and refinement of the new typology and tool may lead to greater clarity in definition and design of interventions. This should support the development of a reliable and coherent evidence base on interventions to promote interprofessional working in health and social care.