Evaluation of Two Web-Based Alcohol Interventions for Mandated College Students

This study evaluated the efficacy of two web-based interventions aimed at reducing heavy drinking in mandated college students. Mandated students were randomly assigned to one of two conditions: web-based personalized normative feedback (WPNF) or web-based education (WE). As predicted, results indicated mandated students in the WPNF condition reported significantly greater reductions in weekly drinking quantity, peak alcohol consumption, and frequency of drinking to intoxication than students in the WE condition at a 30-day follow-up. Although not statistically significant, there was a similar trend for changes in alcohol-related problems. Mandated students in the WPNF group also reported significantly greater reductions in estimates of peer drinking from baseline to the follow-up assessment than students in the WE group. Additionally, changes in estimates of peer drinking mediated the effect of the intervention on changes in drinking. Findings provide support for providing web-based personalized normative feedback as an intervention program for mandated college students

Educating School Nurses to Improve Bowel Continence in Children with Spina Bifida

Children born with spina bifida, a neural tube defect, can have associated loss of bowel control resulting in bowel incontinence. The National Patient Spina Bifida Registry found that 87% of those living with spina bifida had bowel incontinence, and less than 30% were bowel continent (Sawin et al., 2015; Wiener et al., 2017). Unfortunately, providers may never start a child living with spina bifida on a bowel management program. Evidence suggests that children with spina bifida should begin a bowel management program early, using a stepwise approach. School nurses, who interact with children living with spinal bifida while attending school, have an opportunity to provide support to children living with bowel incontinence but may lack knowledge and skills on bowel management. This evidence-based practice project aimed to educate school nurses in a county school district about the best bowel management guidelines for children attending school with spina bifida. The project’s goal was to improve the lives of children with spina bifida, especially in school, by motivating school nurses to play a more active role in the child’s bowel management routines towards increasing bowel continence. The Iowa Model of Evidence-Based Practice guided the steps of the project. Eighty-six school nurses received education about spina bifida and bowel management asynchronously via an online voiceover PowerPoint presentation. Nurses completed a pre-and post-knowledge test, and a significant improvement (p \u3c 0.001) in test scores by approximately three points was seen from the pre-test to the post-test. Educating school nurses about spina bifida and bowel management made them better prepared to support and manage bowel incontinence in children living with spina bifida

Adult Attachment as a Risk Factor for Intimate Partner Violence: The “Mispairing” of Partners’ Attachment Styles

This study examined the relationship between intimate partner violence and adult attachment in a sample of 70 couples. The attachment style of each partner and the interaction of the partners\u27 attachment styles were examined as predictors of intimate partner violence. Additional analyses were conducted to examine violence reciprocity and to explore differences in the relationship between attachment and violence using continuous and dichotomous violence measures. Results of hierarchical regression analyses indicated the mispairing of an avoidant male partner with an anxious female partner was associated with both male and female violence. When controlling for partner violence, the relationship between attachment and violence was significant for males only. In addition, analyses using a dichotomized violence variable produced different results from analyses using a continuous violence measure. Clinical implications include focusing on the discrepancy between partners’ needs for intimacy and distance within the couple as a strategy for treating intimate partner violenc

Effects of short-term treatment with atorvastatin in smokers with asthma - a randomized controlled trial

<b>Background</b> The immune modulating properties of statins may benefit smokers with asthma. We tested the hypothesis that short-term treatment with atorvastatin improves lung function or indices of asthma control in smokers with asthma.<p></p> <b>Methods</b> Seventy one smokers with mild to moderate asthma were recruited to a randomized double-blind parallel group trial comparing treatment with atorvastatin (40 mg per day) versus placebo for 4 weeks. After 4 weeks treatment inhaled beclometasone (400 ug per day) was added to both treatment arms for a further 4 weeks. The primary outcome was morning peak expiratory flow after 4 weeks treatment. Secondary outcome measures included indices of asthma control and airway inflammation.<p></p> <b>Results</b> At 4 weeks, there was no improvement in the atorvastatin group compared to the placebo group in morning peak expiratory flow [-10.67 L/min, 95% CI -38.70 to 17.37, p=0.449], but there was an improvement with atorvastatin in asthma quality of life score [0.52, 95% CI 0.17 to 0.87 p=0.005]. There was no significant improvement with atorvastatin and inhaled beclometasone compared to inhaled beclometasone alone in outcome measures at 8 weeks.<p></p> <b>Conclusions</b> Short-term treatment with atorvastatin does not alter lung function but may improve asthma quality of life in smokers with mild to moderate asthma. Clinicaltrials.gov identifier: NCT0046382

Handler beliefs affect scent detection dog outcomes

Our aim was to evaluate how human beliefs affect working dog outcomes in an applied environment. We asked whether beliefs of scent detection dog handlers affect team performance and evaluated relative importance of human versus dog influences on handlers’ beliefs. Eighteen drug and/or explosive detection dog/handler teams each completed two sets of four brief search scenarios (conditions). Handlers were falsely told that two conditions contained a paper marking scent location (human influence). Two conditions contained decoy scents (food/toy) to encourage dog interest in a false location (dog influence). Conditions were (1) control; (2) paper marker; (3) decoy scent; and (4) paper marker at decoy scent. No conditions contained drug or explosive scent; any alerting response was incorrect. A repeated measures analysis of variance was used with search condition as the independent variable and number of alerts as the dependent variable. Additional nonparametric tests compared human and dog influence. There were 225 incorrect responses, with no differences in mean responses across conditions. Response patterns differed by condition. There were more correct (no alert responses) searches in conditions without markers. Within marked conditions, handlers reported that dogs alerted more at marked locations than other locations. Handlers’ beliefs that scent was present potentiated handler identification of detection dog alerts. Human more than dog influences affected alert locations. This confirms that handler beliefs affect outcomes of scent detection dog deployments

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing