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The involvement of men in efforts to challenge men’s violence is a crucial component for eradicating gender-based violence (GBV) and for disrupting the continued responsibilisation of women and survivors for addressing the problem at various scales. But as men’s participation in the field has evolved and become increasingly professionalised, so tensions have emerged regarding what happens when men enter women-majority professional and movement anti-violence spaces. Via a feminist discourse analysis, this article explores how men active in the violence against women and girls (VAWG) sector and movement conceptualise and negotiate the challenges associated with the reproduction of patriarchal privilege in the context of their work or activism. Analysis points to how gender inequalities and masculine norms are both instrumentalised as well as entrenched, even when men ‘allies’ seek to challenge them. Moreover, findings indicate how men’s often elevated status in anti-violence practice and movement spaces can be used to resource a type of ‘entrepreneurial masculinity’ which obstructs structural change as regards gendered norms and expectations. This article offers an empirical and theoretical contribution to the expanding literature on men’s role(s) in the prevention of men’s violence against women and minoritised genders, and the ways in which gendered privilege operates therein.

Objective Treatment of obesity-related diseases, rather than obesity itself, remains the mainstay of medical care. The current study examined a novel approach that prioritizes weight management in primary care to shift this paradigm. Methods PATHWEIGH is a weight management approach consisting of staff team training, workflow system management, and data capture from tools built into the electronic medical record (EPIC). PATHWEIGH was compared to standard of care (SOC) using two family medicine clinics in the same US healthcare system. Descriptive statistics compared patient-, provider-, and clinic-level factors between the groups among those with at least one weight-prioritized visit (WPV) and one follow-up weight over 14 months. Results Groups were similar in terms of total patient visits (7,353 vs. 7,984) and patients eligible for a WPV (i.e. >18 years + body mass index >25 kg/m2; 3,746 vs. 3,008, PATHWEIGH vs. SOC, respectively). However, more PATHWEIGH clinic patients (15.9% vs. 8.4%; P < 0.001) received at least one WPV. Although no difference was observed for average patient weight loss over 14 months (P = 0.991), the number of WPVs per patient was higher in PATHWEIGH (P < 0.001) and significantly associated with weight loss (P = 0.001), with an average decrease in weight of 0.55 kg per additional visit. Conclusions Results from the current study demonstrate early success in changing the paradigm from treating weight-related comorbidities to treating weight in primary care.

Effective and widely available therapies are still needed for outpatients with COVID-19. We aimed to evaluate the efficacy and safety of lopinavir/ritonavir for early treatment of non-hospitalized individuals diagnosed with COVID-19.This randomized, placebo-controlled, double-blind, multi-site decentralized clinical trial enrolled non-hospitalized adults with confirmed SARS-CoV-2 infection and six or fewer days of acute respiratory infection symptoms who were randomized to either twice daily oral lopinavir/ritonavir (400 mg/100 mg) or placebo for 14 days. Daily surveys on study days 1 through 16 and again on study day 28 evaluated symptoms, daily activities, and hospitalization status. The primary outcome was longitudinal change in an ordinal scale based on combination of symptoms, activity, and hospitalization status through Day 15 and was analyzed by use of a Bayesian longitudinal proportional odds logistic regression model for estimating the probability of a superior recovery for lopinavir/ritonavir over placebo (odds ratio [OR]1).Between June 2020 and December 2021, 448 participants were randomized to receive either lopinavir/ritonavir (n=216) or placebo (n=221). The mean symptom duration prior to randomization was 4.3 days [SD 1.3]. There were no differences between treatment groups through the first 15 days for the ordinal primary outcome (OR 0.96; 95% CrI: 0.66 to 1.41). There were 3.2% (n=7) of lopinavir/ritonavir and 2.7% (n=6) of placebo participants hospitalized by day 28. Serious adverse events did not differ between groups.Lopinavir/ritonavir did not significantly improve symptom resolution or reduce hospitalization in non-hospitalized participants with COVID-19.ClinicalTrials.gov Identifier: NCT04372628.

BACKGROUND Management of sinonasal malignancy (SNM) often includes surgical resection as part of the multimodality treatment. Treatment-related surgical morbidity can occur, yet risk factors associated with complications in this population have not been sufficiently investigated. METHODS Adult patients with histologically confirmed SNM whose primary treatment included surgical resection were prospectively enrolled into an observational, multi-institutional cohort study from 2015 to 2020. Sociodemographic, disease, and treatment data were collected. Complications assessed included cerebrospinal fluid leak, orbital injury, intracranial injury, diplopia, meningitis, osteoradionecrosis, hospitalization for neutropenia, and subsequent chronic rhinosinusitis. The surgical approach was categorized as endoscopic resection (ER) or open/combined resection (O/CR). Associations between factors and complications were analyzed using Student's t test, Fisher's exact test, and logistic regression modeling. RESULTS Overall, 142 patients met the inclusion criteria. Twenty-three subjects had at least 1 complication (16.2%). On unadjusted analysis, adjuvant radiation therapy was associated with developing a complication (91.3% vs 65.5%, p = 0.013). Compared with the ER group (n = 98), the O/CR group (n = 44) had a greater percentage of higher T-stage lesions (p = 0.004) and more frequently received adjuvant radiation (84.1% vs 64.4%, p = 0.017) and chemotherapy (50.0% vs 30.6%, p = 0.038). Complication rates were similar between the ER and O/CR groups without controlling for other factors. Regression analysis that retained certain factors showed O/CR was associated with increased odds of experiencing a complication (odds ratio, 3.34; 95% confidence interval, 1.06-11.19). CONCLUSIONS Prospective, multicenter evaluation of SNM treatment outcomes is feasible. Undergoing O/CR was associated with increased odds of developing a complication after accounting for radiation therapy. Further studies are warranted to build upon these findings.

Background Coronavirus disease 2019 (COVID-19) has a heterogeneous outcome in individuals from remaining asymptomatic to death. In a majority of cases, mild symptoms are present that do not require hospitalization and can be successfully treated in the outpatient setting, though symptoms may persist for a long duration. We hypothesize that drugs suitable for decentralized study in outpatients will have efficacy among infected outpatients Methods The TREAT NOW platform is designed to accommodate testing multiple agents with the ability to incorporate new agents in the future. TREAT NOW is an adaptive, blinded, multi-center, placebo-controlled superiority randomized clinical trial which started with two active therapies (hydroxychloroquine and lopinavir/ritonavir) and placebo, with the hydroxychloroquine arm dropped shortly after enrollment began due to external evidence. Each arm has a target enrollment of 300 participants who will be randomly assigned in an equal allocation to receive either an active therapy or placebo twice daily for 14 days with daily electronic surveys collected over days 1 through 16 and on day 29 to evaluate symptoms and a modified COVID-19 ordinal outcome scale. Participants are enrolled remotely by telephone and consented with a digital interface, study drug is overnight mailed to study participants, and data collection occurs electronically without in-person interactions. Discussion If effective treatments for COVID-19 can be identified for individuals in the outpatient setting before they advance to severe disease, it will prevent progression to more severe disease, reduce the need for hospitalization, and shorten the duration of symptoms. The novel decentralized, “no touch” approach used by the TREAT NOW platform has distinction advantages over traditional in-person trials to reach broader populations and perform study procedures in a pragmatic yet rigorous manner. Trial registration ClinicalTrials.gov NCT04372628. Registered on April 30, 2020. First posted on May 4, 2020.

Health advances are contingent on continuous development of new methods and approaches to foster data-driven discovery in the biomedical and clinical sciences. Open-science and team-based scientific discovery offer hope for tackling some of the difficult challenges associated with managing, modeling, and interpreting of large, complex, and multisource data. Translating raw observations into useful information and actionable knowledge depends on effective domain-independent reproducibility, area-specific replicability, data curation, analysis protocols, organization, management and sharing of health-related digital objects. This study expands the functionality and utility of an ensemble semi-supervised machine learning technique called Compressive Big Data Analytics (CBDA). Applied to high-dimensional data, CBDA (1) identifies salient features and key biomarkers enabling reliable and reproducible forecasting of binary, multinomial and continuous outcomes (i.e., feature mining); and (2) suggests the most accurate algorithms/models for predictive analytics of the observed data (i.e., model mining). The method relies on iterative subsampling, combines function optimization and statistical inference, and generates ensemble predictions for observed univariate outcomes. The novelty of this study is highlighted by a new and expanded set of CBDA features including (1) efficiently handling extremely large datasets (>100,000 cases and >1,000 features); (2) generalizing the internal and external validation steps; (3) expanding the set of base-learners for joint ensemble prediction; (4) introducing an automated selection of CBDA specifications; and (5) providing mechanisms to assess CBDA convergence, evaluate the prediction accuracy, and measure result consistency. To ground the mathematical model and the corresponding computational algorithm, CBDA 2.0 validation utilizes synthetic datasets as well as a population-wide census-like study. Specifically, an empirical validation of the CBDA technique is based on a translational health research using a large-scale clinical study (UK Biobank), which includes imaging, cognitive, and clinical assessment data. The UK Biobank archive presents several difficult challenges related to the aggregation, harmonization, modeling, and interrogation of the information. These problems are related to the complex longitudinal structure, variable heterogeneity, feature multicollinearity, incongruency, and missingness, as well as violations of classical parametric assumptions. Our results show the scalability, efficiency, and usability of CBDA to interrogate complex data into structural information leading to derived knowledge and translational action. Applying CBDA 2.0 to the UK Biobank case-study allows predicting various outcomes of interest, e.g., mood disorders and irritability, and suggests new and exciting avenues of evidence-based research in the context of identifying, tracking, and treating mental health and aging-related diseases. Following open-science principles, we share the entire end-to-end protocol, source-code, and results. This facilitates independent validation, result reproducibility, and team-based collaborative discovery.