Multiethnic Perspectives of Shared Decision-Making in Hypertension: A Mixed-Methods Study.

BACKGROUND: Shared decision-making (SDM) has the potential to improve hypertension care quality and equity. However, research lacks diverse representation and evidence about how race and ethnicity affect SDM. Therefore, this study aims to explore SDM in the context of hypertension management. METHODS AND RESULTS: Explanatory sequential mixed-methods design was used. Quantitative data were sourced at baseline and 12-month follow up from RICH LIFE (Reducing Inequities in Care of Hypertension: Lifestyle Improvement for Everyone) participants (n=1212) with hypertension. Qualitative data were collected from semistructured individual interviews, at 12-month follow-up, with participants (n=36) selected based on their SDM scores and blood pressure outcome. Patients were cross- categorized based on high or low SDM scores and systolic blood pressure reduction of ≥10 or <10 mm Hg. Multinomial logistic regression analysis showed that predictors of SDM scores and blood pressure outcome were race and ethnicity (relative risk ratio [RRR], 1.64; P=0.029), age (RRR, 1.03; P=0.002), educational level (RRR, 1.87; P=0.016), patient activation (RRR, 0.98; P<0.001; RRR, 0.99; P=0.039), and hypertension knowledge (RRR, 2.2; P<0.001; and RRR, 1.57; P=0.045). Qualitative and mixed-methods findings highlight that provider-patient communication and relationship influenced SDM, being emphasized both as facilitators and barriers. Other facilitators were patients' understanding of hypertension; clinicians' interest in the patient, and clinicians' personality and attitudes; and barriers included perceived lack of compassion, relationship hierarchy, and time constraints. CONCLUSIONS: Participants with different SDM scores and blood pressure outcomes varied in determinants of decision and descriptions of contextual factors influencing SDM. Results provide actionable information, are novel, and expand our understanding of factors influencing SDM in hypertension.

Development and Preliminary Validation of the Patient Outcome Planning Calculator (POP-C): A Tool for Determining Treatment Dosage in Applied Behavior Analysis.

Board certified behavior analysts (BCBA) are responsible for determining the medically necessary treatment dosage for patients (i.e., the number of hours of therapy a patient should receive per week to optimize progress) during applied behavior analysis (ABA) therapy. However, because there is currently no standard method for making these determinations, BCBAs must rely on their own clinical judgment. Given that clinical judgment may be underdeveloped in some BCBAs, particularly those who are newly certified, more formal strategies are needed to guide decision making as it relates to medical necessity and treatment dosage. In this article we describe the development of the Patient Outcome Planning Calculator (POP-C), a standardized decision-making tool designed to assist novice practitioners in determining the medically necessary ABA treatment intensity and appropriate treatment setting for individuals with autism spectrum disorder (ASD). We present preliminary reliability data as well as construct validity data indicating statistically significant correlations between the POP-C and several norm-referenced and criterion-referenced assessments commonly used to estimate skill level and the corresponding degree of support needed within the ASD population to inform the ABA treatment model and goals. Supplementary Information: The online version contains supplementary material available at 10.1007/s40617-023-00861-6.

Stochastic calculus-guided reinforcement learning: A probabilistic framework for optimal decision-making.

Stochastic Calculus-guided Reinforcement learning (SCRL) is a new way to make decisions in situations where things are uncertain. It uses mathematical principles to make better choices and improve decision-making in complex situations. SCRL works better than traditional Stochastic Reinforcement Learning (SRL) methods. In tests, SCRL showed that it can adapt and perform well. It was better than the SRL methods. SCRL had a lower dispersion value of 63.49 compared to SRL's 65.96. This means SCRL had less variation in its results. SCRL also had lower risks than SRL in the short- and long-term. SCRL's short-term risk value was 0.64, and its long-term risk value was 0.78. SRL's short-term risk value was much higher at 18.64, and its long-term risk value was 10.41. Lower risk values are better because they mean less chance of something going wrong. Overall, SCRL is a better way to make decisions when things are uncertain. It uses math to make smarter choices and has less risk than other methods. Also, different metrics, viz training rewards, learning progress, and rolling averages between SRL and SCRL, were assessed, and the study found that SCRL outperforms well compared to SRL. This makes SCRL very useful for real-world situations where decisions must be made carefully.•By leveraging mathematical principles derived from stochastic calculus, SCRL offers a robust framework for making informed choices and enhancing performance in complex scenarios.•In comparison to traditional SRL methods, SCRL demonstrates superior adaptability and efficacy, as evidenced by empirical tests.

A Rehabilitation Algorithm After Lateral Ankle Sprains in Professional Football (Soccer): An Approach Based on Clinical Practice Guidelines.

Lateral ankle sprain (LAS) is one of the most common types of injury in professional football (soccer) players with high risk of recurrence. The rehabilitation after LAS in professional football players is often still time-based and relies on anecdotal experience of clinicans. There is still a lack of utilization of criteria-based rehabilitation concepts after LAS in professional football. The aims of this clinical commentary are (1) to critically discuss the need for criteria-based rehabilitation concepts after LAS in professional football players, (2) to highlight the current lack of these approaches and (3) to present a novel clinical guideline-based rehabilitation algorithm. Short time-loss (15 days) and high recurrence rate (17%) raise the question of trivialization of LAS in professional football. Despite consequences for many stakeholders involved (players, teams, clubs, insurers), there is still a lack of of criteria-based, step-by-step approaches. The use of a criteria-based rehabilitation approach might reduce the high recurrence rate after LAS in professional football players and will lead, in turn, to increased long-term player availability. Practical experiences of he authors demonstrate the feasibility of such an approach. The effectiveness of this novel rehabilitation algorithm remains to be evaluated in future studies. Level of Evidence: 5.

Emergent perceptual biases from state-space geometry in trained spiking recurrent neural networks.

A stimulus held in working memory is perceived as contracted toward the average stimulus. This contraction bias has been extensively studied in psychophysics, but little is known about its origin from neural activity. By training recurrent networks of spiking neurons to discriminate temporal intervals, we explored the causes of this bias and how behavior relates to population firing activity. We found that the trained networks exhibited animal-like behavior. Various geometric features of neural trajectories in state space encoded warped representations of the durations of the first interval modulated by sensory history. Formulating a normative model, we showed that these representations conveyed a Bayesian estimate of the interval durations, thus relating activity and behavior. Importantly, our findings demonstrate that Bayesian computations already occur during the sensory phase of the first stimulus and persist throughout its maintenance in working memory, until the time of stimulus comparison.

Leveraging emotional intelligence to foster proactive climate change adaptation: A study of engineering decision-making.

The impacts of climate change and development present significant challenges and complexities that require new solutions, wise choices, and multi-disciplinary integration. In this context, emotional intelligence (EI) plays a crucial role. However, traditional engineering education and practice overlook the importance of understanding and managing emotions. This research aims to determine the impact of EI as a tool to enhance proactive decision-making and implement sustainable measures within the engineering profession.The study makes three main research contributions. First, it confirms a positive relationship between EI and proactive sustainable decision-making among engineers. This means that engineers with high EI are more likely to consider the impacts of their decisions on various stakeholders and dimensions of sustainability. Second, it suggests that EI can enhance creativity and innovative thinking in engineering, helping engineers to develop effective solutions for challenges related to climate change. Third, the study advocates for incorporating EI training and assessment into engineering curriculums to foster a sustainable and ethical engineering culture. By improving EI, engineers can enhance their interpersonal skills, self-awareness, and emotional management, which in turn can significantly improve teamwork in addressing challenges related to climate change.

Usability testing of the Set Brave Goals app, a continence goal-selection app for children with spina bifida.

INTRODUCTION: The Set Brave Goals app is the first digital health app (DHA) aimed at helping children with spina bifida (SB) aged 8-17 years old to select and track their urinary and fecal continence goals. Developed by children, parents, providers and researchers, its usability, or appropriateness to a purpose ("user-friendliness"), remains unknown. In an alpha testing stage, our aim was to determine the usability of the app prior to clinical use. METHODS: We recruited children with SB and their parents in clinic and via social media. A recruitment goal of 10 children exceeded industry standard of 5 participants (10 participants are expected to identify approximately 96% of usability problems). Participants downloaded and used the app for a week. They completed a questionnaire, including the System Usability Scale (SUS) and closed and open-ended questions. The SUS is a 10-item validated usability questionnaire most widely used in DHA usability testing (scores range from 0 to 100, 100 representing greatest usability). Median/mean SUS scores for DHAs are 68. Mann-Whitney-U and t-tests were used. RESULTS: Ten children with SB participated (median age: 14 years old, 6 female, 8 shunted, 8 using bladder catheterizations). Twenty parents participated (17 mothers, median age: 42 years old). Median SUS score was 77.5 for children and 73.8 for parents, corresponding to "good" to "excellent" usability (Summary Figure). There were no significant differences between child/parent scores (p = 0.69) or those for other DHAs (p = 0.11). It took a median 5-10 min to go through the app (2 parents felt it took too much time). All participants felt the app was easy to understand and use (100%). Most participants would recommend it to children and parents (children: 89%, parents: 80%), and believed it would be valuable to children (90%, 75%) and urologists (80%, 80%). Free text responses related to more varied color schemes, keeping free-text notes within the app and more flexible goal/alarm setting. These changes were incorporated into the app. COMMENT: Usability testing prior to clinical launch identified areas for app improvement. Although this study sample met industry standards, findings are limited by a small group of participants. The app will undergo further refinement during prospective beta testing. CONCLUSIONS: The Set Brave Goals app for children with SB has acceptable usability parameters. This justifies proceeding with wider use to formalize children's continence goal setting/tracking and testing its value to children with SB and their healthcare providers.

Repurposing Drugs to Treat Novel Infections: A Proposal for a Decision-Making Framework for Clinicians.

PURPOSE: Global fears regarding future epidemics of new and re-emerging infections will prompt clinicians to try out unconventional treatments based on limited evidence, including the repurposing of existing drugs. The dilemma involves balancing clinical intuition with the need to rely on low-quality information because of the scarcity of definitive evidence. An example was ivermectin; with its potential antiviral properties, it was promoted for its efficacy in treating coronavirus disease 2019 despite conflicting outcomes in clinical trials and varying expert opinions. This article describes the development of a decision-making framework to resolve such dilemmas. METHODS: The case study from Sri Lanka illustrates multiple challenges faced by clinicians. As the horrific details of deaths in countries such as Italy spread on social media, there was panic and an unprecedented demand for clinicians and health services to provide effective treatment. This led to the popularity of drugs such as ivermectin and several herbal cures. However, there was no consensus among experts on the efficacy of ivermectin, which eventually led to the authorities to recommend limited approval for use under physician supervision. FINDINGS: The situation lent itself to a framework with 4 elements: prerequisites, applying an appropriate decision-making tool (eg, multiple criteria decision-making methods), ethical considerations, and sensitive communication. IMPLICATIONS: We propose this framework for clinicians when they face similar situations with demands to repurpose medicines with inconclusive evidence of efficacy to combat devastating infections from new or re-emerging infections.

Family planning decision-making in relation to psychiatric disorders in women: a qualitative focus group study.

BACKGROUND: Recent studies revealed an elevated likelihood of unintended pregnancies among women with psychiatric disorders compared to their counterparts without such vulnerability. Despite the importance of understanding family planning decision-making in this group, qualitative inquiries are lacking. This study explored family planning decisions among women with psychiatric disorders. METHODS: Utilizing a qualitative approach, three focus group discussions were conducted with purposive sampling: women with a history of unintended pregnancies (N = 3), women without children (N = 5), and women with a history of intended pregnancies (N = 9), all of whom had self-reported psychiatric disorders. Using thematic framework analysis, we investigated the themes "Shadow of the past," reflecting past experiences, and "Shadow of the future," reflecting future imaginaries, building upon the existing "Narrative Framework." RESULTS: The Narrative Framework formed the foundation for understanding family planning among women with psychiatric disorders. The retrospective dimension of focus group discussions provided opportunities for reflective narratives on sensitive topics, revealing emotions of regret, grief and relief. Childhood trauma, adverse events, and inadequate parenting enriched the "Shadow of the past". The "Shadow of the present" was identified as a novel theme, addressing awareness of psychiatric disorders and emotions toward psychiatric stability. Social influences, stigma, and concerns about transmitting psychiatric disorders shaped future imaginaries in the shadow of the future. CONCLUSIONS: This study enlightens how family planning decision-making in women with psychiatric disorders might be complex, as marked by the enduring impact of past experiences and societal influences in this sample. These nuanced insights underscore the necessity for tailored support for women with psychiatric disorders.

Recent studies show that women with psychiatric disorders are more likely to experience unintended pregnancies. However, the underlying reasons are not fully understood. Understanding those reasons is important to provide better healthcare. Our study explored how women with psychiatric disorders make decisions about family planning.We had conversations with different groups of women­women with unintended pregnancies, women without children, and women with intended pregnancies­through focus group discussions. We partnered with the Dutch mental health organization MIND to capture diverse opinions. Key themes and categories in the discussions were identified and organized.We found four main themes: "Shadow of the past" showed how past events, trauma, and lack of knowledge about parenting affect family planning. "Shadow of the present" revealed different feelings about family planning, the importance of the awareness of psychiatric disorders, and uncertainty about decisions. "Shadow of the future" included thoughts about becoming a mother, the impact of social influences, and concerns about passing on psychiatric disorders. "Reflections on the decision" showed how psychiatric disorders, experiences with motherhood, and feelings of regret, grief and relief had an influence on family planning decisions.In conclusion, our study highlighted the complexity of family planning decisions for women with psychiatric disorders. Past experiences and societal influences, like stigma, play a big role. These insights show the need for personalized family planning support for women with psychiatric disorders.

Cosine similarity and distance measures for p , q - quasirung orthopair fuzzy sets: Applications in investment decision-making.

Similarity measures and distance measures are used in a variety of domains, such as data clustering, image processing, retrieval of information, and recognizing patterns, in order to measure the degree of similarity or divergence between elements or datasets. p , q - quasirung orthopair fuzzy ( p , q - QOF) sets are a novel improvement in fuzzy set theory that aims to properly manage data uncertainties. Unfortunately, there is a lack of research on similarity and distance measure between p , q - QOF sets. In this paper, we investigate different cosine similarity and distance measures between to p , q - quasirung orthopair fuzzy sets ( p , q - ROFSs). Firstly, the cosine similarity measure and the Euclidean distance measure for p , q - QOFSs are defined, followed by an exploration of their respective properties. Given that the cosine measure does not satisfy the similarity measure axiom, a method is presented for constructing alternative similarity measures for p , q - QOFSs. The structure is based on the suggested cosine similarity and Euclidean distance measures, which ensure adherence to the similarity measure axiom. Furthermore, we develop a cosine distance measure for p , q - QOFSs that connects similarity and distance measurements. We then apply this technique to decision-making, taking into account both geometric and algebraic perspectives. Finally, we present a practical example that demonstrates the proposed justification and efficacy of the proposed method, and we conclude with a comparison to existing approaches.

Timing decisions as the next frontier for collective intelligence.

The past decade has witnessed a growing interest in collective decision making, particularly the idea that groups can make more accurate decisions compared with individuals. However, nearly all research to date has focused on spatial decisions (e.g., food patches). Here, we highlight the equally important, but severely understudied, realm of temporal collective decision making (i.e., decisions about when to perform an action). We illustrate differences between temporal and spatial decisions, including the irreversibility of time, cost asymmetries, the speed-accuracy tradeoff, and game theoretic dynamics. Given these fundamental differences, temporal collective decision making likely requires different mechanisms to generate collective intelligence. Research focused on temporal decisions should lead to an expanded understanding of the adaptiveness and constraints of living in groups.

The implications of hormone treatment for cancer risk, screening and treatment in transgender individuals.

There is evidence that gender-affirming hormone treatment (GAHT) for transgender individuals modulates their risk for specific malignancies including breast and prostate cancer, and meningiomas. However, there is insufficient data to make precise risk estimates accounting for age and inherited cancer risk. As such, screening recommendations remain broad. Even less evidence exists for best practice in the management of active or historical cancers in the transgender population. Guidance is therefore mainly extrapolated from cisgender populations but with considerations of the significant benefits of GAHT in the face of any hormonal risk. Clinical experience, the multidisciplinary team and shared decision making with the patient are vital in providing person-centred care, while further research is acquired.

Methods and approaches for developing plain language versions of guidelines recommendations: Scoping review protocol.

AIMS AND OBJECTIVES: This is a protocol of a scoping review that will aim to synthesise methodological evidence on formulating plain language versions of recommendations from guidelines both for clinical practice and for public health. METHOD: We will conduct a search in MEDLINE (Ovid), Embase (Ovid) databases, and webpages of guidelines developers with no language and date limitations. The title/abstract and full-text screening will be performed by two reviewers independently. The team of reviewers will extract data on methods used for developing plain language versions of recommendations in a standardised manner. The data analysis and synthesis will be presented narratively in tabular form. RESULTS AND CONCLUSION: We will conduct a scoping review based on this protocol.

Precision medicine: Externally validated explainable AI support tool for predicting sustainability of infliximab and vedolizumab in ulcerative colitis.

OBJECTIVE: Drug sustainability (DS), a surrogate marker for drug efficacy, is important, especially when aiming for precision medicine. However, it lacks reliable prediction methods. AIMS: To develop and externally validate a web-based artificial intelligence(AI)-derived tool for predicting DS of infliximab and vedolizumab in patients with moderate-to-severe Ulcerative Colitis (UC). METHODS: Data from three Israeli centers included infliximab or vedolizumab patients treated for >54 weeks. Sustainability meant no corticosteroids, hospitalizations or surgeries. Machine learning techniques predicted >54-week and overall DS using baseline clinical data. RESULTS: The model was developed using data from 246 patients from Rabin Medical Center and externally validated on 67 patients from Rambam Health Care Campus and Sheba Medical Center. No significant difference in DS was observed across the datasets. Most patients were biologic-naïve and primarily treated with vedolizumab. The model performed well, with an area under the ROC curve of 0.86, and showed good accuracy (65.5 %-76.9 %) across the test sets. CONCLUSIONS: The study introduces a novel, AI-based tool for predicting >54-week DS of infliximab and vedolizumab in moderate-to-severe UC, using baseline parameters. This can aid clinical decision-making in the framework of precision medicine, promising to optimize disease management while maintaining physician autonomy.

Insight and the Capacity to Refuse Treatment with Electroconvulsive Therapy.

All Australian jurisdictions have statutory provisions governing the use of electroconvulsive therapy. Cases in which the patient lacks insight into their psychotic illness and need for treatment and refuses to have ECT are particularly poignant. In Re ICO [2023] QMHC 1, the Queensland Mental Health Court considered whether a patient with a treatment-resistant psychotic illness had decision-making capacity to refuse ECT. The Court also considered whether the patient had been provided with an adequate explanation of the proposed treatment including the expected benefits, risks and adverse effects of ECT. As well as deciding whether ECT was appropriate in the circumstances, the Court considered whether there were alternative treatments including another trial of the oral antipsychotic clozapine. This article reviews issues relating to lack of insight in persons with psychotic illness and relevant considerations for determining capacity to decline ECT.

Informed Consent and the Duty to Warn: More than the Mere Provision of Information.

Before providing any form of medical treatment, medical practitioners are generally required to discharge their duty to warn. It is argued in this article that the duty to warn, at least as it relates to frail and elderly patients, requires the principles of shared decision-making to be adopted. Doing so will ensure a comprehensive biopsychosocial understanding of the patient and assist in identifying material risks that may not be readily apparent. Such risks include risks that threaten the patient's values, preferences, treatment aims and long-term outcomes. Once such risks are identified, in discharging the duty to warn, they should be contextualised in a manner that makes clear how that risk will manifest in that particular patient. These risks should then also be synthesised within the context of their other medical issues and longer-term interests. Finally, it is suggested that the traditional consent process may need restructuring.

The ReCoDe addiction research consortium: Losing and regaining control over drug intake-Findings and future perspectives.

Substance use disorders (SUDs) are seen as a continuum ranging from goal-directed and hedonic drug use to loss of control over drug intake with aversive consequences for mental and physical health and social functioning. The main goals of our interdisciplinary German collaborative research centre on Losing and Regaining Control over Drug Intake (ReCoDe) are (i) to study triggers (drug cues, stressors, drug priming) and modifying factors (age, gender, physical activity, cognitive functions, childhood adversity, social factors, such as loneliness and social contact/interaction) that longitudinally modulate the trajectories of losing and regaining control over drug consumption under real-life conditions. (ii) To study underlying behavioural, cognitive and neurobiological mechanisms of disease trajectories and drug-related behaviours and (iii) to provide non-invasive mechanism-based interventions. These goals are achieved by: (A) using innovative mHealth (mobile health) tools to longitudinally monitor the effects of triggers and modifying factors on drug consumption patterns in real life in a cohort of 900 patients with alcohol use disorder. This approach will be complemented by animal models of addiction with 24/7 automated behavioural monitoring across an entire disease trajectory; i.e. from a naïve state to a drug-taking state to an addiction or resilience-like state. (B) The identification and, if applicable, computational modelling of key molecular, neurobiological and psychological mechanisms (e.g., reduced cognitive flexibility) mediating the effects of such triggers and modifying factors on disease trajectories. (C) Developing and testing non-invasive interventions (e.g., Just-In-Time-Adaptive-Interventions (JITAIs), various non-invasive brain stimulations (NIBS), individualized physical activity) that specifically target the underlying mechanisms for regaining control over drug intake. Here, we will report on the most important results of the first funding period and outline our future research strategy.

A reliable jumping-based classification methodology for environment sector.

Decision-makers have consistently developed a range of classification models, each possessing unique features within the domain of intelligent models. These endeavors are all directed toward achieving the highest levels of accuracy. In recent developments, two notable methodologies-reliable modeling and jumping modeling approaches-offer specific advantages in formulating cost functions and have been recognized for their role in enhancing classifier accuracy. Specifically, the jumping methodology is based on aligning the learning process with the discrete nature of the classification goal, while the reliable methodology integrates the reliability factor into the learning paradigm. However, their innovative combination, leveraging both accuracy and reliability factors in guiding learning processes, leads to the creation of a high-performing classifier. This addresses a research gap in tackling classification challenges, which remains the core focus of the present study. To evaluate the performance of the proposed reliable jumping-based intelligent classifier in environmental decision-making, we considered ten benchmark datasets spanning various application domains. The numerical results demonstrate that the proposed Reliable Jumping-based intelligent classifier consistently outperforms traditional intelligent classifiers across all studied cases. As a result, the proposed approach proves to be a viable and effective alternative to other intelligent methods in environmental applications.

Comparing Methods for Identifying Post-Market Patient Preferences at the Point of Decision-Making: Insights from Patients with Chronic Pain Considering a Spinal Cord Stimulator Device.

Purpose: To compare three methods for identifying patient preferences (MIPPs) at the point of decision-making: analysis of video-recorded patient-clinician encounters, post-encounter interviews, and post-encounter surveys. Patients and Methods: For the decision of whether to use a spinal cord stimulator device (SCS), a video coding scheme, interview guide, and patient survey were iteratively developed with 30 SCS decision-making encounters in a tertiary academic medical center pain clinic. Burke's grammar of motives was used to classify the attributed source or justification for a potential preference for each preference block. To compare the MIPPs, 13 patients' encounters with their clinician were video recorded and subsequently analyzed by 4 coders using the final video coding scheme. Six of these patients were interviewed, and 7 surveyed, immediately following their encounters. Results: For videos, an average of 66 (range 33-106) sets of utterances potentially indicating a patient preference (a preference block), surveys 33 (range 32-34), and interviews 25 (range 18-30) were identified. Thirty-eight unique themes (75 subthemes), each a preference topic, were identified from videos, surveys 19 themes (12 subthemes), and interviews 39 themes (54 subthemes). The proportion of preference blocks that were judged as expressing a preference that was clearly important to the patient or affected their decision was highest for interviews (72.8%), surveys (68.0%), and videos (27.0%). Videos mostly attributed preferences to the patient's situation (scene) (65%); interviews, the act of receiving or living with SCS (43%); surveys, the purpose of SCS (40%). Conclusion: MIPPs vary in the type of preferences identified and the clarity of expressed preferences in their data sets. The choice of which MIPP to use depends on projects' goals and resources, recognizing that the choice of MIPP may affect which preferences are found.