Support Tool Research Articles

Preventing Eviction-Caused Homelessness through ML-Informed Distribution of Rental Assistance

Rental assistance programs provide individuals with financial assistance to prevent housing instabilities caused by evictions and avert homelessness. Since these programs operate under resource constraints, they must decide who to prioritize. Typically, funding is distributed by a reactive allocation process that does not systematically consider risk of future homelessness. We partnered with Anonymous County (PA) to explore a proactive and preventative allocation approach that prioritizes individuals facing eviction based on their risk of future homelessness. Our ML models, trained on state and county administrative data accurately identify at-risk individuals, outperforming simpler prioritization approaches by at least 20% while meeting our equity and fairness goals across race and gender. Furthermore, our approach would reach 28% of individuals who are overlooked by the current process and end up homeless. Beyond improvements to the rental assistance program in Anonymous County, this study can inform the development of evidence-based decision support tools in similar contexts, including lessons about data needs, model design, evaluation, and field validation.

D3S: Decision support system for sectorization

Sectorization problems refer to dividing a large set, area or network into smaller parts concerning one or more objectives. A decision support system (DSS) is a relevant tool for solving these problems, improving optimisation procedures, and finding feasible solutions more efficiently. This paper presents a new web-based Decision Support System for Sectorization (D3S). D3S is designed to solve sectorization problems in various areas, such as school and health districting,planning sales territories and maintenance operations zones, or political districting. Due to its generic design, D3S bridges the gap between sectorization problems and a state-of-the-art decision support tool. The paper aims to present the generic and technical attributes of D3S by providing detailed information regarding the problem-solution approach (based on Evolutionary Algorithms), objectives (most common in sectorization), constraints, structure and performance.

Learning to view golfing bodies: developing professional vision through video analysis and embodied reenactments

ABSTRACT Inspired by recent research that investigates how coaches interpret performance through the support of video tools and analysis, this article explores how the embodied reenactments of a golf coach shape their student’s interpretation of golfing bodies. More specifically, this research investigates a pedagogical encounter a Professional Golf Association (PGA) coach has with their golf student while engaging in collective in-the-moment video analysis of their student’s performance. Using Goodwin’s (1994) notion of professional vision, this article shows how a golf coach’s embodied methods attune their student’s observation of golfing practices to modern and standard methods for viewing and analyzing golfing bodies. I leverage interaction analysis to unearth and interpret the embodied and interactional nature of their pedagogical encounter, and to better understand how the golf coach’s reenactments shape their student’s professional vision. This research suggests that the body is a fundamental element in teaching and developing professional vision in athletic and performance-based contexts, and provides an avenue for better understanding how coaches, and experts in other professional settings, can apprentice novices’ interpretation and professional vision of practices.

Abstract 1307: Community-driven support platform BlueHQ: An innovative solution to address the needs of patients with colorectal cancer

Abstract Background: Colorectal cancer (CRC) is the third-most commonly diagnosed cancer and the second-leading cause of cancer death in men and women combined in the United States. To manage the disease, patients and caregivers turn to various tools and resources for financial assistance, information dissemination, emotional and peer support, and treatment planning, among other critical issues and needs they face. The Colorectal Cancer Alliance (the Alliance) created BlueHQ to provide patients and caregivers with a resource solution encompassing all potential areas of need. Methods: A survey aimed at understanding needs, barriers, and quality improvement opportunities was disseminated via the Alliance's Blue Hope Nation community for two weeks and by email. One hundred sixty-seven colorectal cancer patients and forty caregivers of colorectal cancer patients completed the survey. Results: Patients and caregiver respondents report reliance on external tools and resources to manage disease and support information. Of the support methods, patient respondents reported using social media (46.11%); notebooks, journals, and planners (45.41%); online articles (44.91%); the Blue Hope Nation Facebook group (41.32%); cancer discussion boards (35.33%); and spiritual or religious advice (29.94%). Patients sought financial assistance, local peer support and groups, and mental health help to minimize diagnosis-related stressors. To provide more efficient care and limit stress, the community suggested financial help, communication and support from other patients and caregivers, and informational resources. Conclusions: These survey results are consistent with other published learnings about patient and caregiver reliance on support tools and highlight the myriad resources that patients and caregivers currently use for managing CRC. BlueHQ is a support solution that provides patients and caregivers with access to information and resources tailored to users’ specified profiles, including molecular characteristics of the tumor. Users can also store documents and information from healthcare encounters, creating a longitudinal record and tracking disease progression. In the age of precision oncology, BlueHQ ensures patients and users are presented with personalized educational materials and multimedia resources to help manage their disease and bridge the gaps in patient support. BlueHQ was explicitly designed to connect the CRC community to the latest therapeutic and clinical research resources to improve outcomes and quality of life for all CRC patients. Citation Format: Kimberly Lynn Newcomer. Community-driven support platform BlueHQ: An innovative solution to address the needs of patients with colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1307.

Economic evaluation of a trial exploring the effects of a web-based support tool for parents of children with juvenile idiopathic arthritis.

To explore the cost-effectiveness of a web-based support tool for parents of children with Juvenile Idiopathic Arthritis. A multi-centred randomised controlled trial was conducted in paediatric rheumatology centres in England. The WebParC intervention consisted of online information about JIA and its treatment and a toolkit using cognitive-behavioural therapy principles to support parents manage their child's JIA. An economic evaluation was performed alongside the trial involving 220 parents. The primary outcome was the self-report Pediatric Inventory for Parents measure of illness-related parenting stress, with two dimensions; difficulty and frequency. These measures along with costs were assessed post intervention at 4 months and 12 months. Costs were calculated for healthcare usage using a UK NHS economic perspective. Data was collected and analysed on the impact of caring costs on families. Uncertainty around cost effectiveness was explored using bootstrapping and cost-effectiveness acceptability curves. The intervention arm showed improved average Pediatric Inventory for Parents scores for the dimensions of frequency and difficulty, of 1.5 and 3.6 respectively at 4 months and. 0.35 and 0.39 at 12 months, representing improved PIP scores for the intervention arm. At both 4 and 12 month follow up the average total cost per case was higher in the control group when compared with the intervention arm with mean differences of £360 (95% CI £29.6 to £691) at 4 months and £203 (95% CI £16 to £390) at 12 months. The probability of the intervention being cost effective ranged between 49% and 54%. The WebParC intervention led to reductions in primary and secondary healthcare resource use and costs at 4 and 12 months. The intervention demonstrated particular savings for rheumatology services at both follow ups. Future economies of scale could be realised by health providers with increased opportunities for cost effectiveness over time. ISRCTN, ISRCTN13159730.

Abstract 2317: Development and validation of a colorectal cancer prediction model: A nationwide cohort-based study

Abstract Background: Colorectal cancer (CRC) ranks as the third most prevalent cancer globally and is a major cause of cancer-related mortality. Early diagnosis is critical to increase survival rates. While CRC screening has shown significant benefit, adherence remains low, and there is a need for better tools to identify high-risk patients. Risk prediction models were demonstrated to identify such patients. Aim: To establish an individualized risk prediction model for CRC diagnosis based on Electronic Health Records (EHR). Methods: This is a retrospective cohort study utilizing EHR data of Clalit Health Services (CHS) members aged 50-74 that were eligible for CRC screening, from January 2013 to January 2019. The model was trained to predict CRC diagnosis within two years using approximately 20,000 EHR features including socio-demographic information, laboratory results and medical history. Model performance, as a complementary screening method, was evaluated. Results: The study included 2679 subjects with CRC diagnosis and 1,133,713 subjects without CRC diagnosis. The model was trained on subjects from 2013-2017, and performance was validated on subjects from 2019 and a cohort of subjects that underwent fecal occult blood test (FOBT). Incidence values of CRC among subjects in the top 1% risk scores were higher than baseline (2.3% vs. 0.3%; lift 8.38; P-value <0.001). Characteristics of subjects by risk scores percentiles are presented in Table 1. Cumulative event probabilities increased with higher model scores, indicating a correlation between predicted and actual risk of CRC diagnosis. Model-based risk stratification among subjects with a positive FOBT, identified subjects with more than twice the risk for CRC compared to FOBT alone. Conclusions: We developed an individualized risk prediction model for CRC that can be utilized as a complementary decision support tool for healthcare providers to precisely identify patients at high risk for CRC and refer them to confirmatory testing. Citation Format: Samah Hayek. Development and validation of a colorectal cancer prediction model: A nationwide cohort-based study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2317.

Clinical decision support tools useful for identifying sepsis risk.

Evaluate the effectiveness of the clinical decision support tools (CDSTs), POC Advisor (POCA), and Modified Early Warning System (MEWS) in identifying sepsis risk and influencing time to treatment for inpatients, comparing their respective alert mechanisms. This study was conducted at two academic university medical center hospitals. Data from adult inpatients in medical-surgical and telemetry units were analyzed from January 1, 2020, to December 31, 2020. Criteria included sepsis-related ICD-10 codes, antibiotic administration, and ordered sepsis labs. Subsequent statistical analyses utilized Fisher's exact test and Wilcoxon Rank Sum test, focusing on mortality differences by age, sex, and race/ethnicity. Among 744 patients, 143 sepsis events were identified, with 83% already receiving treatment upon CDST alert. Group 1 (POCA alert) showed reduced response time compared with MEWS, while Group 3 (MEWS) experienced longer time to treatment. Group 4 included sepsis events missed by both systems. Mortality differences were not significant among the groups. While CDSTs play a role, nursing assessment and clinical judgment are crucial. This study recognized the potential for alarm fatigue due to a high number of CDST-driven alerts, while emphasizing the importance of a collaborative approach for prompt sepsis treatment and potential reduction in sepsis-related mortality.

Game-theoretic analysis of competition between access service providers utilizing a Nash genetic algorithm

Fiber-to-the-home (FTTH) technology is a promising solution for providing advanced service delivery to end-users, but its implementation requires substantial capital expenditures. To minimize investment risks and aid decision-making for access service providers, we propose a game theoretic framework based on a modified Nash genetic algorithm. We illustrate how this framework can be applied to analyze the competition between access providers offering a flat-rate FTTH service on multiple geographical areas. Each provider determines its price for all areas simultaneously and decides whether to invest on a particular area depending on the anticipated revenues. Two distinct demand models are adopted to describe different types of consumer behavior. A solution engine, based on a modified mixed-variable Nash genetic algorithm is implemented under an open-source license. The significance and practical implications of the equilibrium points obtained for both single and multiple area games are discussed. The proposed framework and the solution engine developed, aid both providers and regulatory bodies to analyze competitive environments. They can also be used to implement decision support tools for similar problems as well. The paper concludes by pointing out further research directions in this context.

Forecasting Hospital Room and Ward Occupancy Using Static and Dynamic Information Concurrently: Retrospective Single-Center Cohort Study.

Predicting the bed occupancy rate (BOR) is essential for efficient hospital resource management, long-term budget planning, and patient care planning. Although macro-level BOR prediction for the entire hospital is crucial, predicting occupancy at a detailed level, such as specific wards and rooms, is more practical and useful for hospital scheduling. The aim of this study was to develop a web-based support tool that allows hospital administrators to grasp the BOR for each ward and room according to different time periods. We trained time-series models based on long short-term memory (LSTM) using individual bed data aggregated hourly each day to predict the BOR for each ward and room in the hospital. Ward training involved 2 models with 7- and 30-day time windows, and room training involved models with 3- and 7-day time windows for shorter-term planning. To further improve prediction performance, we added 2 models trained by concatenating dynamic data with static data representing room-specific details. We confirmed the results of a total of 12 models using bidirectional long short-term memory (Bi-LSTM) and LSTM, and the model based on Bi-LSTM showed better performance. The ward-level prediction model had a mean absolute error (MAE) of 0.067, mean square error (MSE) of 0.009, root mean square error (RMSE) of 0.094, and R2 score of 0.544. Among the room-level prediction models, the model that combined static data exhibited superior performance, with a MAE of 0.129, MSE of 0.050, RMSE of 0.227, and R2 score of 0.600. Model results can be displayed on an electronic dashboard for easy access via the web. We have proposed predictive BOR models for individual wards and rooms that demonstrate high performance. The results can be visualized through a web-based dashboard, aiding hospital administrators in bed operation planning. This contributes to resource optimization and the reduction of hospital resource use.

Using decision support tools for treatment decision making about antidepressants in outpatient psychiatric consultations

ObjectiveTo examine the use of decision support tools in decision making about antidepressants during conversations between patients with major depressive disorder (MDD) and their psychiatrists. MethodsTheme-oriented discourse analysis of two psychiatric consultation groups: control (n = 17) and intervention (n = 16). In the control group, only a doctor’s conversation guide was used; in the intervention group, the conversation guide and a patient decision aid (PDA) were used. ResultsPsychiatrists mainly dominated conversations in both consultation groups. They were less likely to elicit patient treatment-related perspectives in the intervention group as they focused more on delivering the information than obtaining patient perspectives. However, using PDA in the intervention group slightly encouraged patients to participate in decisional talk. ConclusionThe decision support tools did promote SDM performance. Using the conversation guide in both consultation groups encouraged the elicitation of patient perspectives, which helped the psychiatrists in tailoring their recommendations of options based on patient preferences and concerns. Using the PDA in the intervention group created space for treatment discussion and fostered active collaboration in treatment decision making. Practice ImplicationsOur findings have implications for SDM communication skills training and critical reflection on SDM practice.

Visual inspection problem-solving strategies at different experience levels

Airport security screening is a visual inspection task comprising search and decision. Problem solving is used to support decision making. However, it is not well understood. This study investigated how airport security screeners employ problem solving during x-ray screening, and how strategies change with experience. Thirty-nine professional security screeners were observed performing x-ray screening in the field at an Australian International Airport. Video and eye-tracking data were collected and analysed to explore activity phases and problem-solving strategies. Less-experienced screeners performed more problem solving and preferred problem-solving strategies that rely on visual examination without decision support or that defer decision making, compared to more-experienced screeners, who performed efficient and independent strategies. Findings also show that screeners need more time to develop problem-solving skills than visual scanning skills. Screeners would benefit from problem-solving support tools and intensified training and mentorship within the first six months of experience to advance problem-solving competencies.

Advancing Flood Risk Assessment through Integrated Hazard Mapping: A Google Earth Engine-Based Approach for Comprehensive Scientific Analysis and Decision Support

This study utilises a comprehensive, multi-layered approach to assess flooding susceptibility in a specific area, integrating diverse environmental datasets such as JRC Global Surface Water, Landsat 8 images, and SRTM elevation data. Employing the GEE FMA, a powerful tool leveraging Google Earth Engine capabilities, the analysis covers water occurrence, permanent water, elevation, distance to water, topographic hazard score, and vegetation indices (NDVI and NDWI). The Water Occurrence layer establishes a foundational understanding of water-body distribution’s correlation with flood vulnerability, while Permanent Water refines this understanding. Distance to Water measures proximity for targeted risk evaluation, and Elevation identifies vulnerable regions based on topography. The GEE FMA synthesises these layers into a Flood Hazard Susceptibility map, categorising vulnerability into Very Low, Low, Medium, High, and Very High. This nuanced understanding is crucial for prioritising interventions. The GEE FMA’s rapid processing speed makes it an invaluable tool for short-term decision support in flood hazard disaster management, offering insights for informed decision-making and resilient infrastructure development. The Topographic Hazard Score provides information on how topography influences flood risk, while the Wetness Hazard Score categorises moisture conditions for identifying flood-prone locations. Decision-makers rely on these values for quick and precise flood susceptibility assessments. In an era of climate uncertainties and urbanisation, the GEE FMA emerges as a reliable tool for decision-making, mitigating flood impacts, and developing effective flood risk management strategies.

A system for automatic classification of endodontic treatment quality in CBCT.

An evaluation of the effectiveness of a new computational system proposed for automatic classification, developed based on a Siamese network combined with Convolutional Neural Networks (CNNs), is presented. It aims to identify endodontic technical errors using Cone Beam Computed Tomography (CBCT). The study also aims to compare the performance of the automatic classification system with that of dentists. One thousand endodontically treated maxillary molars sagittal and coronal reconstructions were evaluated for the quality of the endodontic treatment and the presence of periapical hypodensities by three board-certified dentists and by an oral and maxillofacial radiologist. The proposed classification system was based on a Siamese network combined with EfficientNet B1 or EfficientNet B7 networks. Accuracy, sensivity, precision, specificity, and F1-score values were calculated for automated artificial systems and dentists. Chi-square tests were performed. The performances were obtained for EfficienteNet B1, EfficientNet B7 and dentists. Regarding accuracy, sensivity and specificity, the best results were obtained with EfficientNet B1. Concerning precision and F1-score, the best results were obtained with EfficientNet B7. The presence of periapical hypodensity lesions was associated with endodontic technical errors. In contrast, the absence of endodontic technical errors was associated with the absence of hypodensity. Quality evaluation of the endodontic treatment performed by dentists and by Siamese Network combined with EfficientNet B7 or EfficientNet B1 networks was comparable with a slight superiority for the Siamese Network. CNNs have the potential to be used as a support and standardization tool in assessing endodontic treatment quality in clinical practice.

Near-repeat terrorism: Identifying and analyzing the spatiotemporal attack patterns of major terrorist organizations

For many decades, the contagion of terrorism has presented a huge risk to our global society. To this end, previous research has found that single terrorist attacks can elevate the risk of subsequent attacks nearby, an idea referred to as the near-repeat phenomenon. A near-repeat pair consists of two attacks that occur within a specific time period and spatial distance of one another (e.g, one week and 10 miles), and the existence of near-repeats reflects calculated decisions by terrorists as they plan when and where to attack. Thus, enhancing our understanding of these attack patterns can shed light into the operational strategies of terrorist organizations. To this end, there remains key gaps in current knowledge regarding whether near-repeat attack patterns generalize across major terrorist organizations (i.e., do all major organizations exhibit near-repeat activity?), and the major risk factors associated with near-repeat terrorism (e.g., attack tactics). Utilizing data on over 50,000 terrorist attacks, this study seeks to fill these gaps in knowledge by first analyzing near-repeat attack patterns both across and within major terrorist organizations, and subsequently developing a statistical learning pipeline to identify the risk factors which are most salient in near-repeat attacks. We find that near-repeat terrorism occurs at a statistically significant level for 28 out of the 30 organizations studied. Although our findings show that near-repeat attacks are common amongst organizations, we find that the tactics (e.g., weapon choice, target type) utilized within near-repeat attacks vary vastly between organizations. The results of this work offer insights that augment our knowledge of terrorism patterns, which can in turn improve counterterrorism operations. Further, our end-to-end data-driven framework offers a strong decision support tool in which users can first detect near-repeat attacks, and subsequently identify the key operational patterns within those attacks (for any terrorist organizations of interest).

Current and potential use of animal disease data by stakeholders in the global south and north

What cannot be measured will not be managed. The Global Burden of Animal Diseases (GBADs) will generate information on animal disease burdens by species, production system, type and gender of farmer and consumer, geographical region, and time period. To understand the demand for burden of animal disease (BAD) data and how end-users might benefit from this, we reviewed the literature on animal diseases prioritisation processes (ADPP) and conducted a survey of BAD information users. The survey covered their current use of data and prioritizations as well as their needs for different, more, and better information. We identified representative (geography, sector, species) BAD experts from the authors’ networks and publicly available documents and e-mailed 1485 experts. Of 791 experts successfully contacted, 271 responded (34% response rate), and 185 complete and valid responses were obtained. Most respondents came from the public sector followed by academia/research, and most were affiliated to institutions in low- and middle-income countries (LMICs). Of the six ADPPs commonly featured in literature, only three were recognised by more than 40% of experts. An additional 23 ADPPs were used. Awareness of ADDPs varied significantly by respondents. Respondents ranked animal disease priorities. We used exploded logit to combine first, second and third disease priorities to better understand prioritzation and their determinants. Expert priorities differed significantly from priorities identified by the ADDPs, and also from the priorities stated veterinary services as reported in a survey for a World Organisation of Animal Health (WOAH) technical item. Respondents identified 15 different uses of BAD data. The most common use was presenting evidence (publications, official reports, followed by disease management, policy development and proposal writing). Few used disease data for prioritzation or resource allocation, fewer routinely used economic data for decision making, and less than half were aware of the use of decision support tools (DSTs). Nearly all respondents considered current BAD metrics inadequate, most considered animal health information insufficiently available and not evidence-based, and most expressed concerns that decision-making processes related to animal health lacked transparency and fairness. Cluster analysis suggested three clusters of BAD users and will inform DSTs to help them better meet their specific objectives. We conclude that there is a lack of satisfaction with current BAD information, and with existing ADDPs, contributing to sub-optimal decision making. Improved BAD data would have multiple uses by different stakeholders leading to better evidenced decisions and policies; moreover, clients will need support (including DSTs) to optimally use BAD information.

Developing conceptual model for safety risk management in aviation as building capacity and resilience strategy

Purpose This study aims to develop an inclusive, multidisciplinary, flexible and organizationally adaptable safety risk management framework, including diversity management, that will be implemented to ensure safety is and remains at the desired level. If the number of incidents and potential incidents that could lead to accidents and their impact rates are to be reduced operationally and administratively, aviation safety risks and sources of risk must be better understood, sources of risk identified, and the safety risk management framework designed in an organization-specific and organization-wide sustainable way. At this point, it is necessary to draw the conceptual framework well and to define the boundaries of the concepts well. In this study, a framework model that can be adapted to the organization is proposed to optimize the management of risks and provide both efficient and effective resource allocation and organizational structure design in its operations and management functions. Design/methodology/approach The qualitative research method – triple techniques – was deemed appropriate for this study, which aims to identify, examine, interpret and develop the situations of safety management models. In this context, document analysis, business process modeling technique and Delphi techniques from qualitative research methods were used via integration as the methodology of this research. Findings To manage dynamic civil aviation management activities and business processes effectively and efficiently, the risk management process is the building block of the “Proposed Process Model” that supports the decision-making processes of aviation organizations and managers. This “Framework Conceptual Model” building block also helps build capacity and resilience by enabling continuous development, organizational learning, and flexible structuring. Research limitations/implications This research is limited to air transportation and aviation safety management issues. This research is limited specifically to a safety-based risk management framework for the aviation industry. This research may have social implications as source saving, optimum resource use and capacity building will make a contribution to society and add value besides operational and practical implementation. Social implications This research may contribute to more safe operations and functions in the aviation industry. Originality/value Management and academia may gain considerable support from this research to manage their safety risks via a corporate-tailored risk management framework, both improving resilience and developing corporate capacity. With this model presented, decision-makers will have a guiding structure that can optimally manage the main risk types that may be encountered in the safety risk in the fields of suppliers, manufacturers, demand changes, logistics, information management, environmental, legal and regulatory. Existing studies in the literature are generally in the form of algorithms and cannot be used as a decision-making support tool. This model aims to fill the gap in the literature. In addition, added value may be created by applying this model to optimum management safety risks in the real aviation industry and its related sectors.

Reliability of clinical judgment for evaluation of informed consent in mental health settings and the validation of the Evaluation of Informed Consent to Treatment (EICT) scale.

The competence assessment to give informed consent in the legal and healthcare settings is often performed merely through clinical judgment. Given the acknowledged limited reliability of clinician-based evaluation in the mental health sector, particularly for the assessment of competence to consent, our objective was to ascertain the dependability of clinical judgment when evaluating the ability of schizophrenia patients to make choices about their health. The potential convergence between clinical evaluation and scores from a new standardized assessment (the "Evaluation of Informed Consent to Treatment" - "EICT" scale) was therefore tested. The scale assesses four dimensions of competence, specifically how patients normally understand information relating to care (Understanding); how they evaluate the choice of treatment in terms of risk/benefit ratio (Evaluating); how they reason coherently in the decision-making process (Reasoning); and, finally, their ability to make a choice between treatment alternatives (Expressing a choice). Thirty-four outpatients with schizophrenia were evaluated for their competence to consent by five referring clinicians with different backgrounds (psychiatrist, forensic psychiatrist, geriatrician, anesthetist, and medico-legal doctor). Inter-raters variability was tested through correlation analyses between the scores obtained by the clinicians on a modified version of the Global Assessment of Functioning scale (GAF) designed specifically to subjectively assess functioning in each of the four competence dimensions. Two validated competence scales (Mac-CAT-T, SICIATRI-R), and a neuropsychological battery were also administered along with scales for evaluating neuropsychiatric symptoms severity and side effects of medication. Clinical judgments of the individual specialists showed great inter-rater variability. Likewise, only weak/non-significant correlations were found between the EICT subscales and the respective clinicians-rated GAF scales. Conversely, solid correlations were found between the EICT and MacCAT-T subscales. As expected, healthy controls performed better in the ability to give informed consent to treatment, as measured by the three scales (i.e., EICT, MacCAT-T, and SICIATRI-R), and neuropsychological test performance. In the comparisons between patients who, according to the administered EICT, were able or not able to give informed consent to treatment, significant differences emerged for the Phonemic verbal fluency task (p = 0.038), Verbal judgments (p = 0.048), MacCAT-T subscales, and SICIATRI-R total score. Moreover, EICT exhibited excellent internal consistency (Cronbach's alphas ranging from 0.96 to 0.98 for the four subscales) while the Item Analysis, by measuring the correlation between each item of the EICT and the total score, was excellent for all items of all subscales (alphas ranging from 0.86 to 0.98). In conclusion, our findings highlighted that the assessment of competence exclusively through clinical judgment is not fully reliable and needs the support of standardized tools. The EICT scale could therefore be useful in assessing general competence to consent both in healthcare and legal contexts, where it might be necessary to evaluate the effective competence of patients with psychiatric disorders. Finally, this scale could serve as a valuable tool for decisions regarding whether and to what extent a patient needs support.

Machine Learning May Be An Alternative To BIPSS In The Differential Diagnosis Of ACTH-Dependent Cushing's Syndrome.

This study aimed to develop machine learning (ML) algorithms for the differential diagnosis of adrenocorticotropic hormone (ACTH)-dependent Cushing's syndrome (CS) based on biochemical and radiological features. Logistic regression algorithms were used for ML, and the area under the receiver operating characteristics curve (AUROC) was used to measure performance. We used Shapley Contributed Comments (SHAP) values, which help explain the results of the ML models to identify the meaning of each feature and facilitate interpretation. A total of 106 patients, 80 with Cushing's disease (CD) and 26 with ectopic ACTH syndrome (EAS), were enrolled in the study. The ML task was created to classify patients with ACTH-dependent CS into CD and EAS. The average AUROC value obtained in the cross-validation of the logistic regression model created for the classification task was 0.850. The diagnostic accuracy of the algorithm was 86%. The SHAP values indicated that the most important determinants for the model were the 2-day 2-mg dexamethasone suppression test, the > 50% suppression in the 8-mg high-dose dexamethasone test, late-night salivary cortisol, and the diameter of the pituitary adenoma. We have also made our algorithm available to all clinicians via a user-friendly interface. ML algorithms have the potential to serve as an alternative decision support tool to invasive procedures in the differential diagnosis of ACTH-dependent CS.

Clinical perspectives on serum tumor marker use in predicting prognosis and treatment response in advanced non-small cell lung cancer.

The optimal positioning and usage of serum tumor markers (STMs) in advanced non-small cell lung cancer (NSCLC) care is still unclear. This review aimed to provide an overview of the potential use and value of STMs in routine advanced NSCLC care for the prediction of prognosis and treatment response. Radiological imaging and clinical symptoms have shown not to capture a patient's entire disease status in daily clinical practice. Since STM measurements allow for a rapid, minimally invasive, and safe evaluation of the patient's tumor status in real time, STMs can be used as companion decision-making support tools before start and during treatment. To overcome the limited sensitivity and specificity associated with the use of STMs, tests should only be applied in specific subgroups of patients and different test characteristics should be defined per clinical context in order to answer different clinical questions. The same approach can similarly be relevant when developing clinical applications for other (circulating) biomarkers. Future research should focus on the approaches described in this review to achieve STM test implementation in advanced NSCLC care.

WindValue, End-of-Life Decisions for Wind Farms

Wind farms have an estimated lifespan of twenty to twenty five years, and as they age, their value decreases, potentially becoming a liability. This article discusses a research project that aims to develop a decision support tool for wind farm owners. The tool will help estimate financial outcomes and assess risk profiles for different options such as repowering, life extension, or decommissioning. Additionally, the project will explore challenges and opportunities related to co-investment between local communities and wind farm owners.