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.

Cost-utility Analysis of Navigate, a Treatment Decision Aid for Men with Prostate Cancer and Their Partners, in Comparison to Usual Care.

BACKGROUND AND OBJECTIVE: Evidence on the cost effectiveness of decision aids to guide management decisions for men with prostate cancer is limited. We examined the cost utility of the Navigate online decision aid for men with prostate cancer in comparison to usual care (no decision aid). METHODS: A Markov model with a 10-yr time horizon was constructed from a government health care perspective. Data from the Navigate trial (n = 302) and relevant published studies were used for model inputs. Incremental costs and quality-adjusted life-years (QALYs) were calculated for the two strategies. One-way and probabilistic sensitivity analyses were undertaken to address model uncertainty. KEY FINDINGS AND LIMITATIONS: On average, the Navigate strategy was estimated to cost AU$8899 (95% uncertainty interval [UI] AU$7509-AU$10438) and produce 7.08 QALYs (95% UI 6.73-7.36) in comparison to AU$9559 (95% UI AU$8177-AU$11017) and 7.03 QALYs (95% UI 6.67-7.31) or usual care. The Navigate strategy dominated usual care as it produced cost-savings and higher QALYs, although differences for both outcomes were small over 10 yr. The likelihood of Navigate being cost effective at a conventionally acceptable threshold of AU$50000 per QALY gained was 99.7%. This study is limited by the availability, quality, and choice of the data used in the model. CONCLUSIONS AND CLINICAL IMPLICATIONS: Use of an online decision aid for men with prostate cancer appears to be cost effective relative to usual care in Australia, driven by the higher acceptance and uptake of active surveillance. Wider implementation of decision aids may better inform men diagnosed with prostate cancer about their management options. PATIENT SUMMARY: We looked at the cost effectiveness of an online decision aid for guiding Australian men with prostate cancer in choosing a management option. We found that this decision aid was cost effective, mainly because more men chose active surveillance. Decision aids that inform patients about their management options should be more widely used in health care.

Comparison of Khorana vs. ONKOTEV predictive score to individualize anticoagulant prophylaxis in outpatients with cancer.

BACKGROUND: Based on the Khorana score, guidelines recommend anticoagulation for primary prophylaxis (PP) in outpatients with cancer with an intermediate-to-high risk of venous thromboembolism (VTE). ONKOTEV score has been prospectively externally validated as novel risk assessment model (RAM) with good discriminatory performances but no direct comparisons with Khorana Score are available. METHODS: Using the ONKOTEV validation dataset (n = 425), we applied generalized decision curve analysis (gDCA) which integrates the principles of evidence-based medicine with treatment effects, model accuracy and patient preferences (weighted as the relative value [RV] of avoiding VTE versus major bleeding [MB]). The aim is to select the most optimal treatment strategy among multiple options: "no treatment", "treat all patients with DOAC/LMVH", or "use ONKOTEV/KHORANA scores to guide PP with DOAC/LMWH". RESULTS: Results showed that ONKOTEV-guided PP (using DOAC or LMWH) remained the most optimal strategy for wide range assumption of treatment efficacy and patient's preference. For those patients, who value avoiding VTE more than MB, then offering DOAC to all patients represents the best strategy. When MBs are feared more than the morbidity of VTE, ONKOTEV-guided PP (DOAC) represents the best management strategy. In all cases, ONKOTEV outperformed Khorana for individualized VTE prevention. CONCLUSIONS: When the two predictive models are integrated within a decision analysis framework, ONKOTEV appears superior to Khorana Score in guiding individualized prevention of cancer-related VTE in outpatients with cancer. The findings herein reported provide cutting edge insights in cancer care and support the spread of ONKOTEV score in the ambulatory cancer setting.

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.

Decision Regret in Patients with Appendiceal Cancer Undergoing Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy.

BACKGROUND: Decision regret is an emerging patient reported outcome. The aim of this study was to assess the incidence of regret in patients with appendiceal cancer (AC) who underwent cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (CRS-HIPEC). PATIENTS AND METHODS: An anonymous survey was distributed to patients through the Appendix Cancer and Pseudomyxoma Peritonei (ACPMP) Research Foundation. The Decision Regret Scale (DRS) was employed, with DRS > 25 signifying regret. Patient demographics, tumor characteristics, postoperative outcomes, symptoms (FACT-C), and PROMIS-29 quality of life (QoL) scores were compared between patients who regretted or did not regret (NO-REG) the procedure. RESULTS: A total of 122 patients were analyzed. The vast majority had no regret about undergoing CRS-HIPEC (85.2%); 18 patients expressed regret (14.8%). Patients with higher regret had: income ≤ $74,062 (72.2% vs 44.2% NO-REG; p = 0.028), major complications within 30 days of surgery (55.6% vs 15.4% NO-REG; p < 0.001), > 30 days hospital stay (38.9% vs 4.8% NO-REG; p < 0.001), a new ostomy (27.8% vs 7.7% NO-REG; p = 0.03), >1 CRS-HIPEC procedure (56.3% vs 12.6% NO-REG; p < 0.001). Patients with worse FACT-C scores had more regret (p < 0.001). PROMIS-29 QOL scores were universally worse in patients with regret. Multivariable analysis demonstrated > 30 days in the hospital, new ostomy and worse gastrointestinal symptom scores were significantly associated with regret. CONCLUSIONS: The majority of patients with AC undergoing CRS-HIPEC do not regret undergoing the procedure. Lower income, postoperative complications, an ostomy, undergoing > 1 procedure, and with worse long-term gastrointestinal symptoms were associated with increased regret. Targeted perioperative psychological support and symptom management may assist to ameliorate regret.

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.

The neural representation of self, close, and famous others: An electrophysiological investigation on the social brain.

It is well established that the Self has a unique representation in the social brain, as evident from the Self-Referential Effect (SRE). However, the timing and neural mechanisms underlying the representation of individuals with varying degrees of closeness and emotional relevance to the Self remain unclear. Twenty-two participants read 260 personality traits and decided whether they described themselves, a close friend, or an admired celebrity. A strong Self-Referential Effect (SRE) was found at behavioral, ERP, and neuroimaging levels. Three anterior ERP components were identified as sensitive to social information: a P200 (250-350 ms) responding to famous others' traits, a P600 (500-700 ms) responding to self-trait processing, and a late positivity (800-950 ms) responding to self-trait processing and close traits. Source reconstructions revealed partially overlapping but distinct neural sources for each individual. The right precuneus (bodily self) and inferior frontal areas (inner voice) were active only during self-processing, while the right medial prefrontal cortex (BA10) was consistently active across tasks, showing a robust SRE. These findings provide insights into the neural mechanisms underlying the representation of the Self in social contexts.

Automatic rehabilitation exercise task assessment of stroke patients based on wearable sensors with a lightweight multichannel 1D-CNN model.

Approximately 75% of stroke survivors have movement dysfunction. Rehabilitation exercises are capable of improving physical coordination. They are mostly conducted in the home environment without guidance from therapists. It is impossible to provide timely feedback on exercises without suitable devices or therapists. Human action quality assessment in the home setting is a challenging topic for current research. In this paper, a low-cost HREA system in which wearable sensors are used to collect upper limb exercise data and a multichannel 1D-CNN framework is used to automatically assess action quality. The proposed 1D-CNN model is first pretrained on the UCI-HAR dataset, and it achieves a performance of 91.96%. Then, five typical actions were selected from the Fugl-Meyer Assessment Scale for the experiment, wearable sensors were used to collect the participants' exercise data, and experienced therapists were employed to assess participants' exercise at the same time. Following the above process, a dataset was built based on the Fugl-Meyer scale. Based on the 1D-CNN model, a multichannel 1D-CNN model was built, and the model using the Naive Bayes fusion had the best performance (precision: 97.26%, recall: 97.22%, F1-score: 97.23%) on the dataset. This shows that the HREA system provides accurate and timely assessment, which can provide real-time feedback for stroke survivors' home rehabilitation.

Evaluating the impact of the global evidence, local adaptation (GELA) project for enhancing evidence-informed guideline recommendations for newborn and young child health in three African countries: a mixed-methods protocol.

BACKGROUND: Poverty-related diseases (PRD) remain amongst the leading causes of death in children under-5 years in sub-Saharan Africa (SSA). Clinical practice guidelines (CPGs) based on the best available evidence are key to strengthening health systems and helping to enhance equitable health access for children under five. However, the CPG development process is complex and resource-intensive, with substantial scope for improving the process in SSA, which is the goal of the Global Evidence, Local Adaptation (GELA) project. The impact of research on PRD will be maximized through enhancing researchers and decision makers' capacity to use global research to develop locally relevant CPGs in the field of newborn and child health. The project will be implemented in three SSA countries, Malawi, South Africa and Nigeria, over a 3-year period. This research protocol is for the monitoring and evaluation work package of the project. The aim of this work package is to monitor the various GELA project activities and evaluate the influence these may have on evidence-informed decision-making and guideline adaptation capacities and processes. The specific project activities we will monitor include (1) our ongoing engagement with local stakeholders, (2) their capacity needs and development, (3) their understanding and use of evidence from reviews of qualitative research and, (4) their overall views and experiences of the project. METHODS: We will use a longitudinal, mixed-methods study design, informed by an overarching project Theory of Change. A series of interconnected qualitative and quantitative data collections methods will be used, including knowledge translation tracking sheets and case studies, capacity assessment online surveys, user testing and in-depth interviews, and non-participant observations of project activities. Participants will comprise of project staff, members of the CPG panels and steering committees in Malawi, South Africa and Nigeria, as well as other local stakeholders in these three African countries. DISCUSSION: Ongoing monitoring and evaluation will help ensure the relationship between researchers and stakeholders is supported from the project start. This can facilitate achievement of common goals and enable researchers in South Africa, Malawi and Nigeria to make adjustments to project activities to maximize stakeholder engagement and research utilization. Ethical approval has been provided by South African Medical Research Council Human Research Ethics Committee (EC015-7/2022); The College of Medicine Research and Ethics Committee, Malawi (P.07/22/3687); National Health Research Ethics Committee of Nigeria (01/01/2007).

Artificial Intelligence, the Digital Surgeon: Unravelling Its Emerging Footprint in Healthcare - The Narrative Review.

Background: Artificial Intelligence (AI) holds transformative potential for the healthcare industry, offering innovative solutions for diagnosis, treatment planning, and improving patient outcomes. As AI continues to be integrated into healthcare systems, it promises advancements across various domains. This review explores the diverse applications of AI in healthcare, along with the challenges and limitations that need to be addressed. The aim is to provide a comprehensive overview of AI's impact on healthcare and to identify areas for further development and focus. Main Applications: The review discusses the broad range of AI applications in healthcare. In medical imaging and diagnostics, AI enhances the accuracy and efficiency of diagnostic processes, aiding in early disease detection. AI-powered clinical decision support systems assist healthcare professionals in patient management and decision-making. Predictive analytics using AI enables the prediction of patient outcomes and identification of potential health risks. AI-driven robotic systems have revolutionized surgical procedures, improving precision and outcomes. Virtual assistants and chatbots enhance patient interaction and support, providing timely information and assistance. In the pharmaceutical industry, AI accelerates drug discovery and development by identifying potential drug candidates and predicting their efficacy. Additionally, AI improves administrative efficiency and operational workflows in healthcare, streamlining processes and reducing costs. AI-powered remote monitoring and telehealth solutions expand access to healthcare, particularly in underserved areas. Challenges and Limitations: Despite the significant promise of AI in healthcare, several challenges persist. Ensuring the reliability and consistency of AI-driven outcomes is crucial. Privacy and security concerns must be navigated carefully, particularly in handling sensitive patient data. Ethical considerations, including bias and fairness in AI algorithms, need to be addressed to prevent unintended consequences. Overcoming these challenges is critical for the ethical and successful integration of AI in healthcare. Conclusion: The integration of AI into healthcare is advancing rapidly, offering substantial benefits in improving patient care and operational efficiency. However, addressing the associated challenges is essential to fully realize the transformative potential of AI in healthcare. Future efforts should focus on enhancing the reliability, transparency, and ethical standards of AI technologies to ensure they contribute positively to global health outcomes.

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.