Analysis of Implementing Best Practices for Co-Prescribing Naloxone in Your Agency Online CME Training Module via Pre- and Post- Knowledge Assessment.

The opioid epidemic poses substantial risk to society. Providers must ensure that their patients understand the uses and risks of both opioids and naloxone. One way to analyze this concept is via metacognition. This refers to a person’s knowledge about cognitive phenomena, and thus it regulates self-awareness abilities in decision making, such as planning and evaluating. It is not only important for providers to have knowledge on best practices, but also to have self-awareness, and confidence in their decision making to ensure optimal patient outcomes. True-false confidence weighted scoring can be utilized, whereby various levels of confidence are assessed from “I am confident this is true,” to “I think, but am unsure, if this is true,” and similarly for false answers. This study analyzed the efficacy of an online training module, “Implementing Best Practices for Co-Prescribing Naloxone in Your Agency” and used a metacognitive analysis approach to determine efficacy. The training module, pre- and post-tests were administered at Inspira Health Network on 9/12/22 and 9/13/22. This analysis finds a significant improvement in pre- and post-intervention scores, as well as significant improvement in provider confidence in their answer choices. Such an analysis provides insight not only to efficacy of an intervention, but also the likelihood of confidence, and continued use of the intervention

Pain and Sleep are Associated in Fibromyalgia Patients

This poster explores whether a correlation exists between lack of sleep and fibromyalgia pain

Preferences for Support Resources Among Loved Ones of Adults Prescribed Opioid Medications

The opioid epidemic continues to be a leading cause of overdose and related deaths in America. While most interventions are focused on the individuals with opioid and substance use disorder (OUD/SUD); the impact caregivers and family can have on the treatment of patients with OUD is non-existent. The purpose of this study is to collect pilot data on peers, friends, and family members of patients with SUD/OUD to understand the barriers in psychosocial support and maintaining treatment retention; barriers to accessing medication assisted therapy (MAT) and naloxone; and caregiver fatigue and barriers for caregivers. The collected data will be used to develop a digital health intervention (DHI) in the form of a mobile application/web page. To develop the survey, a review of the current literature on PubMed relating to OUD/SUD and stigma, caregiver fatigue, efficacy of DHIs, readiness to change, and promoting naloxone use was conducted. The results of the review support the fact that caregivers of patients with OUD/SUD experience fatigue and often do not have accurate knowledge of how to help patients. Furthermore, DHIs were found to improve access to treatment and reduce stigma and associated barriers. The next step of the study will be to recruit caregivers, peers, and family members of individuals with OUD to conduct surveys and development of the DHI

A phase field method for tomographic reconstruction from limited data

Classical tomographic reconstruction methods fail for problems in which there is extreme temporal and spatial sparsity in the measured data. Reconstruction of coronal mass ejections (CMEs), a space weather phenomenon with potential negative effects on the Earth, is one such problem. However, the topological complexity of CMEs renders recent limited data reconstruction methods inapplicable. We propose an energy function, based on a phase field level set framework, for the joint segmentation and tomographic reconstruction of CMEs from measurements acquired by coronagraphs, a type of solar telescope. Our phase field model deals easily with complex topologies, and is more robust than classical methods when the data are very sparse. We use a fast variational algorithm that combines the finite element method with a trust region variant of Newton’s method to minimize the energy. We compare the results obtained with our model to classical regularized tomography for synthetic CME-like images

A marked point process model with strong prior shape information for extraction of multiple, arbitrarily-shaped objects

We define a method for incorporating strong prior shape information into a recently extended Markov point process model for the extraction of arbitrarily-shaped objects from images. To estimate the optimal configuration of objects, the process is sampled using a Markov chain based on a stochastic birth-and-death process defined in a space of multiple objects. The single objects considered are defined by both the image data and the prior information in a way that controls the computational complexity of the estimation problem. The method is tested via experiments on a very high resolution aerial image of a scene composed of tree crowns

A multi-layer `gas of circles' Markov random field model for the extraction of overlapping near-circular objects

We propose a multi-layer binary Markov random field (MRF) model that assigns high probability to object configurations in the image domain consisting of an unknown number of possibly touching or overlapping near-circular objects of approximately a given size. Each layer has an associated binary field that specifies a region corresponding to objects. Overlapping objects are represented by regions in different layers. Within each layer, long-range interactions favor connected components of approximately circular shape, while regions in different layers that overlap are penalized. Used as a prior coupled with a suitable data likelihood, the model can be used for object extraction from images, e.g. cells in biological images or densely-packed tree crowns in remote sensing images. We present a theoretical and experimental analysis of the model, and demonstrate its performance on various synthetic and biomedical images

Fundamental properties of a selected sample of Ap stars: Inferences from interferometric and asteroseismic constraints

Magnetic fields influence the formation and evolution of stars and impact the observed stellar properties. Ap stars (magnetic A-type stars) are a prime example of this. Access to precise and accurate determinations of their stellar fundamental properties, such as masses and ages, is crucial to understand the origin and evolution of fossil magnetic fields. We propose using the radii and luminosities determined from interferometric measurements, in addition to seismic constraints when available, to infer fundamental properties of 14 Ap stars préviously characterised. We used a grid-based modelling approach, employing stellar models computed with the \textsc{cestam} stellar evolution code, and the parameter search performed with the \textsc{aims} optimisation method. The stellar model grid was built using a wide range of initial helium abundances and metallicities in order to avoid any bias originating from the initial chemical composition. The large frequency separations ( Δν ) of HR\,1217 (HD\,24712) and α~Cir (HD\,128898), two rapidly oscillating Ap stars of the sample, were used as seismic constraints. We inferred the fundamental properties of the 14 stars in the sample. The overall results are consistent within 1σ with previous studies, however, the stellar masses inferred in this study are higher. This trend likely originates from the broader range of chemical compositions considered in this work. We show that the use of Δν in the modelling significantly improves our inferences, allowing us to set reasonable constraints on the initial metallicity which is, otherwise, unconstrained. This gives an indication of the efficiency of atomic diffusion in the atmospheres of roAp stars and opens the possibility of characterising the transport of chemical elements in their interiors

Retrospective Analysis on the Susceptibility of Opiate Addiction Based on Prescribed Medications and Chronic Pain Diagnoses

Introduction: Opioid medications have been increasingly prescribed in recent years, particularly to treat individuals with chronic pain. In the U.S., opioid abuse has been declared an epidemic by the Department of Health and Human Services as the number of opioid-related overdoses in 2010 exceeded 16000 and alarmingly continued to rise 15% from 2015 to 2016. Due to opioid dependence and abuse, opioids are a gateway to subsequent drug addiction. Objective: The goal of this project was to identify a link between certain prescribed opiates and a susceptibility for abuse or misuse in patients with chronic pain through a retrospective analysis. Additionally the various opiate dosages were recorded to identify a potential correlation between higher dosing and a tendency for abuse. Methods: The study population was 67 patients of the NeuroMusculoskeletal Institute who had abused or misused opiates and were discharged from clinics for their abuse. The patients\u27 demographic information, diagnoses, and medications were collected and analyzed. Results: Oxycodone HCl had the highest frequency of misuse or abuse in patients discharged from rehabilitation clinics. Among the different dosages, 15mg/day Oxycodone HCl was most frequently abused. Additionally, highest rates of abuse and misuse occurred in 44-66 year old patients. Conclusion: Older adults (44-66y.o) with chronic pain syndrome are at a higher risk of abusing or misusing their opiate medications, particularly if they are prescribed Oxycodone HCl

Adaptive probabilistic models of wavelet packets for the analysis and segmentation of textured remote sensing images

Remote sensing imagery plays an important role in many fields. It has become an invaluable tool for diverse applications ranging from cartography to ecosystem management. In many of the images processed in these types of applications, semantic entities in the scene are correlated with textures in the image. In this paper, we propose a new method of analysing such textures based on adaptive probabilistic models of wavelet packets. Our approach adapts to the principal periodicities present in the textures, and can capture long-range correlations while preserving the independence of the wavelet packet coefficients. This technique has been applied to several remote sensing images, the results of which are presented.