Neural mechanisms underlying competition-induced optimal decisions in individuals with high entrepreneurial intention.

In a rapidly changing and uncertain business environment, individuals with high entrepreneurial intention (HEI) inevitably need to compete or cooperate with others to maximize their gains. However, the effects of competition and cooperation on the risky decision-making and neural mechanisms of individuals with HEI are not clear. By combining the modified Devil Task and electroencephalogram (EEG) technology, the current study showed that a competition context is more likely to motivate optimal decisions and enhance the total decision gains for individuals with HEI than a cooperation context. A positive relationship between the frequency of optimal decisions and the total gains of decision-making for individuals with HEI was also found, and this relationship was mediated by the degree of entrepreneurial intention. The EEG results showed that individuals with HEI made decisions in the competition context with greater P2 amplitude of frontal regions than in the cooperation context, and source localization analyses revealed that this difference in brain activity was manifested in the medial prefrontal cortex. Finally, the results revealed a positive relationship between the P2 amplitude and the degree of entrepreneurial intention of individuals with HEI. Overall, the study suggests that competition is an effective way to motivate individuals with HEI to make optimal decisions and, thus, maximize their profits, providing new perspectives on ways to promote successful entrepreneurship.

Camouflage patterning modulates neural signatures of attention and decision-making.

Many animals rely on visual camouflage to avoid detection and increase their chances of survival. Edge disruption is commonly seen in the natural world, with animals evolving high-contrast markings that are incongruent with their real body outline in order to avoid recognition. While many studies have investigated how camouflage properties influence viewer performance and eye movement in predation search tasks, researchers in the field have yet to consider how camouflage may directly modulate visual attention and object processing. To examine how disruptive coloration modulates attention, we use a visual object recognition model to quantify object saliency. We determine if object saliency is predictive of human behavioural performance and subjective certainty, as well as neural signatures of attention and decision-making. We show that increasing edge disruption not only reduces detection and identification performance but is also associated with a dampening of neurophysiological signatures of attentional filtering. Increased self-reported certainty regarding decisions corresponds with neurophysiological signatures of evidence accumulation and decision-making. In summary, we have demonstrated a potential mechanism by which edge disruption increases the evolutionary fitness of animals by reducing the brain's ability to distinguish signal from noise, and hence to detect and identify the camouflaged animal.

Health, Salud, : Individualizing Behavioral Needs in a Multicultural World.

Diversity within the United States continues to increase, making it imperative that health care providers understand the impact of cultural background on health behaviors and perceptions. These practices promote trusting patient-provider relationships, improve outcomes, and increase patient satisfaction. In this article, we discuss the 3 largest ethnic or racial minority groups in the United States, Hispanics, African Americans, and Asians, and the intersection of culture and health care through the lens of these distinct communities. We also offer behavioral recommendations to increase awareness and knowledge regarding vast cultural variations within our communities while embracing cultural humility.

Does emotional valence affect cognitive performance and neurophysiological response during decision making? A preliminary study.

This study investigated the impact of the emotional valence of external situations (EVES) on cognitive performance and electrophysiological (EEG) responses during decision-making. 26 healthy adults underwent a modified version of the Trier social stress test, performing five interview-style discourses. Each discourse entailed preparing a speech under increasingly stressful conditions. Participants were also exposed to gradually increasing EVES (i.e., an examining committee displaying progressively more negative-connoted emotional facial expressions). In addition, after each speech, participants completed an arithmetic task to test how emotional manipulation affected cognitive performance. Behavioral data (preparation times) and EEG data (frequency bands) were collected to assess stress regulation, stress resilience, and cognitive performance. The results indicate that EVES significantly influenced stress regulation and resilience, as reflected in the behavioral data. Neurophysiological findings showed increased parietal lobe activity (P4) in the theta and delta bands with rising emotional valence, plateauing from the preparation of the second discourse onward. This suggests enhanced emotional processing and attentional demands. However, gamma band activity decreased in P4 during the preparations for the two discourses following the first, indicating a shift of cognitive resources from higher cognitive functions to emotional processing. This highlights the cognitive cost of maintaining performance and stress regulation under emotionally charged conditions. Such findings suggest that emotional valence modulates cognitive performance and that specific neural mechanisms are involved in managing stress responses. The findings underscore the complex relationship between emotion, cognition, and neural mechanisms, offering valuable insights for stress regulation and resilience, and enhancing performance under pressure.

Mapping cognitive biases in multidisciplinary team (MDT) decision-making for cancer care in Scotland: a cognitive ethnography study protocol.

INTRODUCTION: The efficiency of multidisciplinary teams (MDTs) in cancer care hinges on facilitating clinicians' cognitive processes as they navigate complex and uncertain judgements during treatment planning. When systems and workflows are not designed to adequately support human judgement and decision-making, even experts are prone to fallible reasoning due to cognitive biases. Incomplete integration of information or biased interpretations of patient data can lead to clinical errors and delays in the implementation of treatment recommendations. Though their impact is intuitively recognised, there is currently a paucity of empirical work on cognitive biases in MDT decision-making. Our study aims to explicate the impact of such biases on treatment planning and establish a foundation for targeted investigations and interventions to mitigate their negative effects. METHODS AND ANALYSIS: This is a qualitative, observational study. We employ cognitive ethnography, informed by the Distributed Cognition for Teamwork framework to assess and evaluate MDT decision-making processes. The study involves in-person and virtual field observations of hepatopancreaticobiliary and upper gastrointestinal MDTs and interviews with their members over several months. The data generated will be analysed in a hybrid inductive/deductive fashion to develop a comprehensive map of potential cognitive biases in MDT decision processes identifying antecedents and risk factors of suboptimal treatment planning processes. Further, we will identify components of the MDT environment that can be redesigned to support decision-making via development of an MDT workspace evaluation tool. ETHICS AND DISSEMINATION: This project has received management and ethical approvals from NHS Lothian Research and Development (2023/0245) and the University of Edinburgh Medical School ethical review committee (23-EMREC-049). Findings will be shared with participating MDTs and disseminated via a PhD thesis, international conference presentations and relevant scientific journals.

Insights from explainable AI in oesophageal cancer team decisions.

BACKGROUND: Clinician-led quality control into oncological decision-making is crucial for optimising patient care. Explainable artificial intelligence (XAI) techniques provide data-driven approaches to unravel how clinical variables influence this decision-making. We applied global XAI techniques to examine the impact of key clinical decision-drivers when mapped by a machine learning (ML) model, on the likelihood of receiving different oesophageal cancer (OC) treatment modalities by the multidisciplinary team (MDT). METHODS: Retrospective analysis of 893 OC patients managed between 2010 and 2022 at our tertiary unit, used a random forests (RF) classifier to predict four possible treatment pathways as determined by the MDT: neoadjuvant chemotherapy followed by surgery (NACT + S), neoadjuvant chemoradiotherapy followed by surgery (NACRT + S), surgery-alone, and palliative management. Variable importance and partial dependence (PD) analyses then examined the influence of targeted high-ranking clinical variables within the ML model on treatment decisions as a surrogate model of the MDT decision-making dynamic. RESULTS: Amongst guideline-variables known to determine treatments, such as Tumour-Node-Metastasis (TNM) staging, age also proved highly important to the RF model (16.1 % of total importance) on variable importance analysis. PD subsequently revealed that predicted probabilities for all treatment modalities change significantly after 75 years (p < 0.001). Likelihood of surgery-alone and palliative therapies increased for patients aged 75-85yrs but lowered for NACT/NACRT. Performance status divided patients into two clusters which influenced all predicted outcomes in conjunction with age. CONCLUSION: XAI techniques delineate the relationship between clinical factors and OC treatment decisions. These techniques identify advanced age as heavily influencing decisions based on our model with a greater role in patients with specific tumour characteristics. This study methodology provides the means for exploring conscious/subconscious bias and interrogating inconsistencies in team-based decision-making within the era of AI-driven decision support.

Sex-related differences in the clinical course of aortic root and ascending aortic aneurysms: the DisSEXion Study.

BACKGROUND AND AIMS: To explore male-female differences in aneurysm growth and clinical outcomes in a two-centre retrospective Dutch cohort study of adult patients with ascending aortic aneurysm (AscAA). METHODS: Adult patients in whom imaging of an AscAA (root and/or ascending: ≥40 mm) was performed between 2007 and 2022 were included. Aneurysm growth was analysed using repeated measurements at the sinuses of Valsalva (SoV) and tubular ascending aorta. Male-female differences were explored in presentation, aneurysm characteristics, treatment strategy, survival, and clinical outcomes. RESULTS: One thousand eight hundred and fifty-eight patients were included (31.6% female). Median age at diagnosis was 65.4 years (interquartile range: 53.4-71.7) for females and 59.0 years (interquartile range: 49.3-68.0) for males (P < .001). At diagnosis, females more often had tubular ascending aortic involvement (75.5% vs. 70.2%; P = .030) while males more often had SoV involvement (42.8% vs. 21.6%; P < .001). Maximum absolute aortic diameter, at any location, at diagnosis did not differ between females (45.0 mm) and males (46.5 mm; P = .388). In females, tubular ascending growth was faster (P < .001), whereas in males, SoV growth was faster (P = .005), corrected for covariates. Unadjusted 10-year survival was 72.5% [95% confidence interval (CI) 67.8%-77.6%] for females and 78.3% (95% CI 75.3%-81.3%) for males (P = .010). Twenty-three type A dissections occurred, with an incidence rate of 8.2/1000 patient-years (95% CI 4.4-14.1) in females and 2.4/1000 patient-years (95% CI 1.2-4.5) in males [incidence rate ratio females/males: 3.4 (95% CI 1.5-8.0; P = .004)]. CONCLUSIONS: In patients having entered a diagnostic programme, involvement of aortic segments and age- and segment-related growth patterns differ between women and men with AscAA, particularly at an older age. Unravelling of these intertwined observations will provide a deeper understanding of AscAA progression and outcome in women and men and can be used as an evidence base for patient-tailored clinical guideline development.

How signature complexity affects expert and lay ability to distinguish genuine, disguised and simulated signatures.

This study examined how variations in signature complexity affected the ability of forensic document examiners (FDEs) and laypeople to determine whether signatures are authentic or simulated (forged), as well as whether they are disguised. Forty-five FDEs from nine countries evaluated nine different signature comparisons in this online study. Receiver Operating Characteristic (ROC) analyses revealed that FDEs performed in excess of chance levels, but performance varied as a function of signature complexity: Sensitivity (the true-positive rate) did not differ much between complexity levels (i.e., 65% vs. 79% vs. 79% for low vs medium vs high complexity), but specificity (the true-negative rate) was the highest (95%) for the medium complexity signatures and lowest (73%) for low complexity signatures. The specificity of high-complexity signatures (83%) was between these values. The sensitivity for disguised comparisons was only 11% and did not vary across complexity levels. One hundred-one novices also completed the study. A comparison of the area under the ROC curve (AUCs) revealed that FDEs outperformed novices in medium and high-complexity signatures but not low-complexity signatures. Novices also struggled to detect disguised signatures. While these findings elucidate the role of signature complexity in lay and expert evaluations, the error rates observed here may differ from those in forensic practice due to differences in the experimental stimuli and circumstances under which they were evaluated. This investigation of the role of signature complexity in the evaluation process was not intended to estimate error rates in forensic practice.

Exploring Intraoperative Cognitive Biases in Cardiac Surgery Teams.

This study focuses on understanding the influence of cognitive biases in the intra-operative decision-making process within cardiac surgery teams, recognizing the complexity and high-stakes nature of such environments. We aimed to investigate the perceived prevalence and impact of cognitive biases among cardiac surgery teams, and how these biases may affect intraoperative decisions and patient safety and outcomes. A mixed-methods approach was utilized, combining quantitative ratings across 32 different cognitive biases (0 to 100 visual analogue scale), regarding their "likelihood of occurring" and "potential for patient harm" during the intraoperative phase of cardiac surgery. Based on these ratings, we collected qualitative insights on the most-rated cognitive biases from semi-structured interviews with surgeons, anaesthesiologists, and perfusionists who work in a cardiac operating room. A total of 16 participants, including cardiac surgery researchers and clinicians, took part in the study. We found a significant presence of cognitive biases, particularly confirmation bias and overconfidence, which influenced decision-making processes and had the potential for patient harm. Of 32 cognitive biases, 6 were rated above the 75th percentile for both criteria (potential for patient harm, likelihood of occurring). Our preliminary findings provide a first step toward a deeper understanding of the complex cognitive mechanisms that underlie clinical reasoning and decision-making in the operating room. Future studies should further explore this topic, especially the relationship between the occurrence of intraoperative cognitive biases and postoperative surgical outcomes. Additionally, the impact of metacognition strategies (e.g. debiasing training) on reducing the impact of cognitive bias and improving intraoperative performance should also be investigated.

Waste management optimization with NLP modeling and waste-to-energy in a circular economy.

This work presents a methodology integrating Non-Linear Programming (NLP) for multi-objective and multi-period optimization, addressing sustainable waste management and energy conversion challenges. It integrates waste-to-energy (WtE) technologies such as Anaerobic Digestion (AD), Incineration (Inc), Gasification (Gsf), and Pyrolysis (Py), and considers thermochemical, technical, economic, and environmental considerations through rigorous non-linear functions. Using Mexico City as a case study, the model develops waste management strategies that balance environmental and economic aims, considering social impacts. A trade-off solution is proposed to address the conflict between objectives. The economical optimal solution generates 1.79M$ with 954 tons of CO2 emissions while the environmental one generates 0.91M$ and reduces emissions by 54%, where 40% is due to gasification technology. Moreover, the environmentally optimal solution, with incineration and gasification generates 9500 MWh/day and 5960 MWh/day, respectively, demonstrates the capacity of the model to support sustainable energy strategies. Finally, this work presents an adaptable framework for sustainable waste management decision-making.

How users make online privacy decisions in work and personal contexts of use.

With the rising usage of contactless work options since COVID-19, users increasingly share their personal data in digital tools at work. Using an experimental online vignette study (N = 93), we examined users' willingness to use a video conferencing tool, while systematically varying the context of use (personal vs. low trustworthiness work vs. high trustworthiness work) and the type of information shared (low vs. medium vs. high sensitivity). We also assessed users' perceived responsibility in work and personal contexts of use and their self-assessed digital competence. Our results highlight employer trustworthiness as an important factor in the willingness to use a third-party video conferencing tool, with increased willingness to use these tools in work contexts of use with high trustworthiness compared to those with low trustworthiness. This effect seems to be reduced when the data to be shared is of high sensitivity, compared to medium and low sensitivity data. Furthermore, despite reduced responsibility for data protection in work compared to personal contexts of use, the willingness to use a video conferencing tool did not decrease between trustworthy work and personal contexts of use. We discuss our findings and their methodological implications for future research and derive implications for privacy decisions at work.

Shared Decision-Making for Patients with Stroke in Neurocritical Care: A Qualitative Meta-Synthesis.

Decision-making for patients with stroke in neurocritical care is uniquely challenging because of the gravity and high preference sensitivity of these decisions. Shared decision-making (SDM) is recommended to align decisions with patient values. However, limited evidence exists on the experiences and perceptions of key stakeholders involved in SDM for neurocritical patients with stroke. This review aims to address this gap by providing a comprehensive analysis of the experiences and perspectives of those involved in SDM for neurocritical stroke care to inform best practices in this context. A qualitative meta-synthesis was conducted following the methodological guidelines of the Joanna Briggs Institute (JBI), using the thematic synthesis approach outlined by Thomas and Harden. Database searches covered PubMed, CIHAHL, EMBASE, PsycINFO, and Web of Science from inception to July 2023, supplemented by manual searches. After screening, quality appraisal was performed using the JBI Appraisal Checklist. Data analysis comprised line-by-line coding, development of descriptive themes, and creation of analytical themes using NVivo 12 software. The initial search yielded 7,492 articles, with 94 undergoing full-text screening. Eighteen articles from five countries, published between 2010 and 2023, were included in the meta-synthesis. These studies focused on the SDM process, covering life-sustaining treatments (LSTs), palliative care, and end-of-life care, with LST decisions being most common. Four analytical themes, encompassing ten descriptive themes, emerged: prognostic uncertainty, multifaceted balancing act, tripartite role dynamics and information exchange, and influences of sociocultural context. These themes form the basis for a conceptual model offering deeper insights into the essential elements, relationships, and behaviors that characterize SDM in neurocritical care. This meta-synthesis of 18 primary studies offers a higher-order interpretation and an emerging conceptual understanding of SDM in neurocritical care, with implications for practice and further research. The complex role dynamics among SDM stakeholders require careful consideration, highlighting the need for stroke-specific communication strategies. Expanding the evidence base across diverse sociocultural settings is critical to enhance the understanding of SDM in neurocritical patients with stroke.Trial registration This study is registered with PROSPERO under the registration number CRD42023461608.

Challenges and prospects in bridging precision medicine and artificial intelligence in genomic psychiatric treatment.

Precision medicine is transforming psychiatric treatment by tailoring personalized healthcare interventions based on clinical, genetic, environmental, and lifestyle factors to optimize medication management. This study investigates how artificial intelligence (AI) and machine learning (ML) can address key challenges in integrating pharmacogenomics (PGx) into psychiatric care. In this integration, AI analyzes vast genomic datasets to identify genetic markers linked to psychiatric conditions. AI-driven models integrating genomic, clinical, and demographic data demonstrated high accuracy in predicting treatment outcomes for major depressive disorder and bipolar disorder. This study also examines the pressing challenges and provides strategic directions for integrating AI and ML in genomic psychiatry, highlighting the importance of ethical considerations and the need for personalized treatment. Effective implementation of AI-driven clinical decision support systems within electronic health records is crucial for translating PGx into routine psychiatric care. Future research should focus on developing enhanced AI-driven predictive models, privacy-preserving data exchange, and robust informatics systems to optimize patient outcomes and advance precision medicine in psychiatry.

Stochastic heuristics for decisions under risk and uncertainty.

Models of heuristics are often predicated on the desideratum that they should possess no free parameters. As a result, heuristic implementations are usually deterministic and do not allow for any choice errors, as the latter would require a parameter to regulate the magnitude of errors. We discuss the implications of this in light of research that highlights the evidence supporting stochastic choice and its dependence on preferential strength. We argue that, in principle, the existing models of deterministic heuristics should, and can, be quite easily modified to stochastic counterparts through the addition of an error mechanism. This requires a single free parameter in the error mechanism, whilst otherwise retaining the parameter-free cognitive processes in the deterministic component of existing heuristics. We present various types of error mechanisms applicable to heuristics and discuss their comparative virtues and drawbacks, paying particular attention to their impact on model comparisons between heuristics and parameter-rich models.

Utilizing multi-criteria decision-making analysis and 3D visualization techniques for dam site selection and irrigation area identification in Gedeb River, Ethiopia.

Irrigation dams and irrigation suitability analysis is important for optimal water management, crop selection and productivity, water conservation, environmental sustainability, and economic viability in agriculture arena. Thus, the main objectives of this study were to identify a suitable dam site and irrigation area in the Gedeb River, Ethiopia, using Multi-Criteria Decision-Making analysis and 3D Visualization techniques. To identify a suitable dam site, various parametrs such as rainfall, runoff, stream flow, mineral site, faulting areas, landslide site, rock types, elevation points, relief features, soil types were used while to identify a suitable irrigation area, different parametrs such as altitude, slope, soil, geological structure, distance, and land use land cover datasets were used. The necessary dataset which were used to identify a suitable dam site and irrigation area collected from Ethiopian Mapping Authority (EMA), Ethiopian irrigation and energy ministry freely. In addition, for the final irrigation dam site selection and suitable irrigation area in the Gedeb watershed, multi-criteria decision-making method with expert judgment were applied respectively. Based on the study's findings, a suitable irrigation water reservoir dam covering an area of 1886 ha, with a potential water holding capacity of 2,961,145,697 cubic meters was identified. The results also revealed a highly suitable area of 18,362.05 ha, a moderately suitable area of 19,204.05 ha, a marginally suitable area of 2095.25 ha, and a not suitable area of 2.89 ha for the aforementioned purpose. The methodological approach and research findings presented in this study can greatly assist government and non-governmental organization planners and decision-makers in the development of irrigation projects.

Artificial nutrition in cerebrovascular disease, necessity or futility: Case report.

Introduction: Stroke is one of the leading causes of mortality and disability in the world, with clinical manifestations and severe complications that they negatively affect the patient's recovery, contributing to an uncertain prognosis and difficult decisions with bioethical dilemmas such as artificial nutrition in the context of severe stroke. Presentation of the case: A 49-year-old patient with a Cerebrovascular Accident in a chronic vegetative state, tracheostomy, and gastrostomy user, admitted for infectious complications, whom, under therapeutic proportionality, the decision is made, shared by medical staff and family, to withdraw artificial nutrition. Conclusions: Difficult decision-making involves multiple challenges for both the health personnel and the patient and his or her environment. It must be guided by bioethical principles and proportionality in favor of the quality of life and the patient's benefit.

Research on the water-ground multimodal transport emergency scheduling model and decision-making method considering the actual road network inundation situation.

As urbanization continues to accelerate worldwide, urban flooding is becoming increasingly destructive, making it important to improve emergency scheduling capabilities. Compared to other scheduling problems, the urban flood emergency rescue scheduling problem is more complicated. Considering the impact of a disaster on the road network passability, a single type of vehicle cannot complete all rescue tasks. A reasonable combination of multiple vehicle types for cooperative rescue can improve the efficiency of rescue tasks. This study focuses on the urban flood emergency rescue scheduling problem considering the actual road network inundation situation. First, the progress and shortcomings of related research are analyzed. Then, a four-level emergency transportation network based on the collaborative water-ground multimodal transport transshipment mode is established. It is shown that the transshipment points have random locations and quantities according to the actual inundation situation. Subsequently, an interactive model based on hierarchical optimization is constructed considering the travel length, travel time, and waiting time as hierarchical optimization objectives. Next, an improved A* algorithm based on the quantity of specific extension nodes is proposed, and a scheduling scheme decision-making algorithm is proposed based on the improved A* and greedy algorithms. Finally, the proposed decision-making algorithm is applied in a practical example for solving and comparative analysis, and the results show that the improved A* algorithm is faster and more accurate. The results also verify the effectiveness of the scheduling model and decision-making algorithm. Finally, a scheduling scheme with the shortest travel time for the proposed emergency scheduling problem is obtained.

The First Baby Study: What women would like to have known about first childbirth. A mixed-methods study.

BACKGROUND: Although prenatal care providers aim to prepare women for first childbirth, little research has explored retrospectively what birthing people would like to have known before first childbirth. AIM: To describe women's reports of what they would like to have known before first childbirth but feel they were not told. METHODS: This is a secondary analysis of the First Baby Study, a large prospective cohort study conducted in Pennsylvania, USA. Telephone interviews were conducted with 3006 women 1 month after their first childbirth. Women were first asked: "Was there anything that you would have liked to have known before your delivery that you were not told?". If "yes" they were asked a second question: "Please tell me what you would have liked to have known before your delivery". ANALYSIS: A convergent mixed-methods analysis including descriptive analytics to compare characteristics of women by answers to the first question, and qualitative content analysis of women's open-ended answers to the second question. FINDINGS: A total of 441 women (14.7%) reported there was something they would like to have known before their first childbirth. Women described that communication with care providers was their main concern. They would have liked a better understanding of their options before birth, more agency in decision-making, and more information about the topics of their body, their birth, their baby, and what to expect beyond birth. CONCLUSIONS: Results highlight important topics for childbirth education, and the impact of gaps in shared decision-making, patient-provider communication, and supportive care practices for first childbirth, especially where women have identified vulnerabilities.

Shared decision-making in adolescent healthcare: a literature review of ethical considerations.

PURPOSE: Adolescence is a period of growing independence and maturity, within the period of legal minority. As parents or guardians are socially and legally responsible for adolescents' medical decisions, shared decision-making in adolescent healthcare could be ethically challenging. This review aims to identify and map the ethical tensions in shared decision-making in adolescent healthcare. METHODS: We systematically searched the literature following the PRISMA guidelines to identify relevant articles, which were analyzed using the review of reasons methodology Strech and Sofaer (J Med Ethics 38(2):121-6, 2012). RESULTS: We included 38 articles which involved adolescents, healthcare professionals and parents as being the main stakeholders. Shared decision-making was influenced not only by individual stakeholders' characteristics, but by tensions between stakeholder dyads. Most studies supported the involvement of the adolescent in decision-making, depending on their life experience, decision-making capacity and clinical condition. CONCLUSIONS: Shared decision-making in adolescent health is receiving increasing attention. However, questions remain on what this concept entails, the roles and involvement of stakeholders and its practical implementation. WHAT IS KNOWN: • Although adolescents wish to be involved in health decisions, shared decision-making in adolescents is underexplored • Adolescent shared decision-making is different from pediatric and adult shared decision-making, and is ethically complex due to the adolescent's growing autonomy What is new: • Adolescent SDM involves three-way interactions between the adolescent, healthcare professional and parents • In adolescent shared decision-making, involving or excluding a stakeholder and sharing or withholding information are ethically value-laden steps • Research is needed to further understand the roles of adolescents' personal value systems, extended or reconstituted families and decision aids in shared decision-making.

On the Application of Artificial Intelligence/Machine Learning (AI/ML) in Late-Stage Clinical Development.

Whereas AI/ML methods were considered experimental tools in clinical development for some time, nowadays they are widely available. However, stakeholders in the health care industry still need to answer the question which role these methods can realistically play and what standards should be adhered to. Clinical research in late-stage clinical development has particular requirements in terms of robustness, transparency and traceability. These standards should also be adhered to when applying AI/ML methods. Currently there is some formal regulatory guidance available, but this is more directed at settings where a device or medical software is investigated. Here we focus on the application of AI/ML methods in late-stage clinical drug development, i.e. in a setting where currently less guidance is available. This is done via first summarizing available regulatory guidance and work done by regulatory statisticians followed by the presentation of an industry application where the influence of extensive sets of baseline characteristics on the treatment effect can be investigated by applying ML-methods in a standardized manner with intuitive graphical displays leveraging explainable AI methods. The paper aims at stimulating discussions on the role such analyses can play in general rather than advocating for a particular AI/ML-method or indication where such methods could be meaningful.