Patient-centric care in primary immune thrombocytopenia (ITP): shared decision-making and assessment of health-related quality of life.

ABSTRACTImmune thrombocytopenia (ITP), an autoimmune disease characterized by low platelet counts and increased bleeding risk, can impair health-related quality of life (HRQoL), impacting patients' daily lives and mental health. A number of patient-reported outcome (PRO) measures (both generic and specific to ITP) can be used to understand the impact of ITP on HRQoL and generate evidence to guide disease management. As well-developed PRO tools could help in HRQoL assessment, their optimization could help to solidify a patient-centric approach to ITP management. Shared decision-making is a collaborative process between a patient and their healthcare professional in making decisions about care. Treatment decisions based on this shared process between physician and patient are recommended by clinical guidelines. The goal of this narrative review is to discuss treatment decisions with regards to patient-centric ITP management, with a focus on the impact of PRO measures and the process of shared decision-making in practice.

ProtecT-2-D trial protocol: cardiovascular protection in patients with type 2 diabetes and established heart and/or vascular disease at a cardio-metabolic clinic-a randomized controlled trial.

BACKGROUND: Cardiovascular disease remains the primary cause of morbidity and mortality despite advancements in the treatment of patients with type 2 diabetes. Effective diabetes management extends beyond blood glucose control and includes cardiovascular prevention and treatment. However, the conventional healthcare model often emphasizes single-disease-specific management, leading to fragmented care. We aim to establish an affordable Cardio-Metabolic Clinic (CMC) that can provide comprehensive assessment and specialized care with a focus on cardiovascular protection. METHODS: The ProtecT-2-D study is a prospective, randomized control trial at the Cardiovascular Research Unit, Odense University Hospital Svendborg, Denmark. In this study, 1500 participants with type 2 diabetes and cardiovascular disease will be randomly assigned in a 2:1 ratio to receive either the intervention: treatment in the CMC, or the control: standard of care. The Cardio-Metabolic Clinic applies a decision-making algorithm coded with the latest guidelines to evaluate lifestyle factors and manage medical treatment. Health examinations are conducted at baseline and after three years, and clinical events will be assessed through registry and journal audits after five and ten years. The primary outcome is the time to the first occurrence of a composite of cardiovascular deaths, non-fatal acute myocardial infarctions, non-fatal stroke, or hospitalization due to heart failure at a time frame of five years. DISCUSSION: The Cardio-Metabolic Clinic represents a pioneering approach to diabetes management that aims to improve patient outcomes by reducing the cardiovascular disease burden. This study could transform diabetes care and offer a multidisciplinary, cost-effective, and specialized treatment. We need to establish the efficacy and feasibility of a CMC to integrate comparable clinics into broader healthcare systems, and potentially enhance cardiovascular health in patients with type 2 diabetes. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov NCT06203860.

Directed exploration is elevated in affective disorders but reduced by an aversive interoceptive state induction.

Elevated anxiety and uncertainty avoidance are known to exacerbate maladaptive choice in individuals with affective disorders. However, the differential roles of state vs. trait anxiety remain unclear, and underlying computational mechanisms have not been thoroughly characterized. In the present study, we investigated how a somatic (interoceptive) state anxiety induction influences learning and decision-making under uncertainty in individuals with clinically significant levels of trait anxiety. A sample of 58 healthy comparisons (HCs) and 61 individuals with affective disorders (iADs; i.e., depression and/or anxiety) completed a previously validated explore-exploit decision task, with and without an added breathing resistance manipulation designed to induce state anxiety. Computational modeling revealed a pattern in which iADs showed greater information-seeking (i.e., directed exploration; Cohen's d=.39, p=.039) in resting conditions, but that this was reduced by the anxiety induction. The affective disorders group also showed slower learning rates across conditions (Cohen's d=.52, p=.003), suggesting more persistent uncertainty. These findings highlight a complex interplay between trait anxiety and state anxiety. Specifically, while elevated trait anxiety is associated with persistent uncertainty, acute somatic anxiety can paradoxically curtail exploratory behaviors, potentially reinforcing maladaptive decision-making patterns in affective disorders.

Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances.

Artificial intelligence (AI) is transforming spinal imaging and patient care through automated analysis and enhanced decision-making. This review presents a clinical task-based evaluation, highlighting the specific impact of AI techniques on different aspects of spinal imaging and patient care. We first discuss how AI can potentially improve image quality through techniques like denoising or artifact reduction. We then explore how AI enables efficient quantification of anatomical measurements, spinal curvature parameters, vertebral segmentation, and disc grading. This facilitates objective, accurate interpretation and diagnosis. AI models now reliably detect key spinal pathologies, achieving expert-level performance in tasks like identifying fractures, stenosis, infections, and tumors. Beyond diagnosis, AI also assists surgical planning via synthetic computed tomography generation, augmented reality systems, and robotic guidance. Furthermore, AI image analysis combined with clinical data enables personalized predictions to guide treatment decisions, such as forecasting spine surgery outcomes. However, challenges still need to be addressed in implementing AI clinically, including model interpretability, generalizability, and data limitations. Multicenter collaboration using large, diverse datasets is critical to advance the field further. While adoption barriers persist, AI presents a transformative opportunity to revolutionize spinal imaging workflows, empowering clinicians to translate data into actionable insights for improved patient care.

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

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

Futility Interim Analysis Based on Probability of Success Using a Surrogate Endpoint.

In clinical trials with time-to-event data, the evaluation of treatment efficacy can be a long and complex process, especially when considering long-term primary endpoints. Using surrogate endpoints to correlate the primary endpoint has become a common practice to accelerate decision-making. Moreover, the ethical need to minimize sample size and the practical need to optimize available resources have encouraged the scientific community to develop methodologies that leverage historical data. Relying on the general theory of group sequential design and using a Bayesian framework, the methodology described in this paper exploits a documented historical relationship between a clinical "final" endpoint and a surrogate endpoint to build an informative prior for the primary endpoint, using surrogate data from an early interim analysis of the clinical trial. The predictive probability of success of the trial is then used to define a futility-stopping rule. The methodology demonstrates substantial enhancements in trial operating characteristics when there is a good agreement between current and historical data. Furthermore, incorporating a robust approach that combines the surrogate prior with a vague component mitigates the impact of the minor prior-data conflicts while maintaining acceptable performance even in the presence of significant prior-data conflicts. The proposed methodology was applied to design a Phase III clinical trial in metastatic colorectal cancer, with overall survival as the primary endpoint and progression-free survival as the surrogate endpoint.

Cultural, demographic, and other non-demographic factors associated with cancer genetic counseling patients' appointment accompaniment preferences in the United States.

Although the presence of companion(s) in a genetic counseling session can positively influence session dynamics, research has found that some patients prefer to attend their appointments alone. To date, no studies have examined patient accompaniment preferences across different cultural groups in the context of genetic counseling. This quantitative study aimed to identify factors associated with individual preferences in accompaniment at cancer genetic counseling appointments in a sample (N = 130) of Hispanic/Latine (n = 29) and non-Hispanic/Latine White (n = 101) participants at a large academic medical institution. Variables examined included demographics, horizontal and vertical collectivism, and Hispanic and American acculturation. A link to an online questionnaire was emailed to patients who met four criteria: (1) identified as either Hispanic/Latine or non-Hispanic/Latine White; (2) had attended a cancer genetic counseling appointment at UCLA Health to discuss genetic testing options between October 2020 and December 2022; (3) were at least 18 years of age at the time of their appointment; and (4) indicated they were comfortable reading in Spanish or English; responses were anonymous. Logistic regression analyses identified four significant variables in the model associated with accompaniment preferences: individuals with at least one parent born outside of the US, those who attended their appointment in-person, and those with a higher horizontal collectivism score were less likely to want to attend their cancer genetic counseling appointment alone, while the converse was true among those with a higher American acculturation score. These findings highlight cultural and demographic factors that are associated with patient accompaniment preferences unrelated to ethnicity, indicating genetic counselors should not make assumptions regarding accompaniment preferences based solely on cultural or racial/ethnic background. Genetic counselors should incorporate this understanding when assessing patients' accompaniment preferences.

Hospital Staff Perspectives on the Drivers and Challenges in Implementing a Virtual Rehabilitation Ward: Qualitative Study.

Background: Over the past decade, the adoption of virtual wards has surged. Virtual wards aim to prevent unnecessary hospital admissions, expedite home discharge, and enhance patient satisfaction, which are particularly beneficial for the older adult population who faces risks associated with hospitalization. Consequently, substantial investments are being made in virtual rehabilitation wards (VRWs), despite evidence of varying levels of success in their implementation. However, the facilitators and barriers experienced by virtual ward staff for the rapid implementation of these innovative care models remain poorly understood. Objective: This paper presents insights from hospital staff working on an Australian VRW in response to the growing demand for programs aimed at preventing hospital admissions. We explore staff's perspectives on the facilitators and barriers of the VRW, shedding light on service setup and delivery. Methods: Qualitative interviews were conducted with 21 VRW staff using the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework. The analysis of data was performed using framework analysis and the 7 domains of the NASSS framework. Results: The results were mapped onto the 7 domains of the NASSS framework. (1) Condition: Managing certain conditions, especially those involving comorbidities and sociocultural factors, can be challenging. (2) Technology: The VRW demonstrated suitability for technologically engaged patients without cognitive impairment, offering advantages in clinical decision-making through remote monitoring and video calls. However, interoperability issues and equipment malfunctions caused staff frustration, highlighting the importance of promptly addressing technical challenges. (3) Value proposition: The VRW empowered patients to choose their care location, extending access to care for rural communities and enabling home-based treatment for older adults. (4) Adopters and (5) organizations: Despite these benefits, the cultural shift from in-person to remote treatment introduced uncertainties in workflows, professional responsibilities, resource allocation, and intake processes. (6) Wider system and (7) embedding: As the service continues to develop to address gaps in hospital capacity, it is imperative to prioritize ongoing adaptation. This includes refining the process of smoothly transferring patients back to the hospital, addressing technical aspects, ensuring seamless continuity of care, and thoughtfully considering how the burden of care may shift to patients and their families. Conclusions: In this qualitative study exploring health care staff's experience of an innovative VRW, we identified several drivers and challenges to implementation and acceptability. The findings have implications for future services considering implementing VRWs for older adults in terms of service setup and delivery. Future work will focus on assessing patient and carer experiences of the VRW.

Hippocampus and striatum show distinct contributions to longitudinal changes in value-based learning in middle childhood.

The hippocampal-dependent memory system and striatal-dependent memory system modulate reinforcement learning depending on feedback timing in adults, but their contributions during development remain unclear. In a 2-year longitudinal study, 6-to-7-year-old children performed a reinforcement learning task in which they received feedback immediately or with a short delay following their response. Children's learning was found to be sensitive to feedback timing modulations in their reaction time and inverse temperature parameter, which quantifies value-guided decision-making. They showed longitudinal improvements towards more optimal value-based learning, and their hippocampal volume showed protracted maturation. Better delayed model-derived learning covaried with larger hippocampal volume longitudinally, in line with the adult literature. In contrast, a larger striatal volume in children was associated with both better immediate and delayed model-derived learning longitudinally. These findings show, for the first time, an early hippocampal contribution to the dynamic development of reinforcement learning in middle childhood, with neurally less differentiated and more cooperative memory systems than in adults.

A Virtual Reality Simulation to Examine the Relationship Between Post-Traumatic Stress Symptoms and Decision-Making in First Responders.

First responders (FRs) are continuously exposed to critical incidents, considered traumatic events (TEs). This cumulative exposure increases the risk for post-traumatic stress disorder (PTSD). However, there is no evidence about the relationship between PTSD symptoms and emergency decision-making (EDM). The objective of this study was to examine the EDM of FRs during a virtual reality through the simulation of two emergency scenarios to collect data on the reaction time and the number of incorrect decisions. We also assessed PTSD symptoms, TE, and sociodemographics. The sample included 368 Portuguese FRs, were 295 (80.20%) males and 73 (19.80%) females, with a mean age of 33.96 (SD = 9.38). Considering the probable PTSD diagnosis according to the DSM-5, 85 (23.10%) of the FRs met the criteria. These individuals who meet the criteria exhibited higher EDM scores (M = 19.60, SD = 5.99) compared to those without probable PTSD (M = 17.87, SD = .5.66) (F(1, 360) = 5.32, p = .02, partial η2 = .015). We found that TEs had a direct effect on EDM, ß = -.16, Z = -3.74, p < .001), and the pathway of trauma-PTSD symptoms-decision-making an indirect effect, ß = .02, Z = 3.10, p = .002). Individuals exposed to more TEs demonstrated faster and more accurate decision-making in the context of EDM. However, when these individuals developed PTSD symptoms, their decision-making became slower and less accurate. The inclusion of a trauma-informed approach for FRs to prevent individual and job-related consequences is discussed.

The impact of regional disparities on the availability of meningococcal vaccines in the US.

BACKGROUND: In the United States (US), three types of vaccines are available to prevent invasive meningococcal disease (IMD), a severe and potentially fatal infection: quadrivalent conjugate vaccines against serogroups A, C, W, Y (MenACWY), and monovalent vaccines against serogroup B (MenB) as well as a newly licensed pentavalent vaccine (MenABCWY) protecting against serogroup A, B, C, W, and Y. The CDC's Advisory Committee on Immunization Practices (ACIP) routinely recommends MenACWY vaccine for all 11- to 12-year-olds with a booster dose at 16 years. MenB vaccination is recommended based on shared clinical decision-making (SCDM) for 16- to 23-year-olds. Recently, the pentavalent meningococcal vaccine (MenABCWY) was recommended by the ACIP. Meningococcal vaccine uptake is suboptimal across the country, particularly among individuals with lower socioeconomic status (SES), despite these recommendations. The objective of the spatial analyses was to assess the relationship between stocking of MenACWY and MenB vaccines, area-level SES, and state-level policies. METHODS: The number of MenACWY and MenB doses stocked by vaccinators was obtained from IQVIA and the CDC's Vaccine for Children (VFC) program and compiled into a county-level dataset from 2016 to 2019. SES, as measured using the CDC's Social Vulnerability Index (SVI), state-level school recommendations, and universal purchasing programs were among the main county-level covariates included to control for factors likely influencing stocking. Data were stratified by public and private market. Bayesian spatial regression models were developed to quantify the variations in rates of stocking and the relative rates of stocking of both vaccines. RESULTS: After accounting for county-level characteristics, lower SES counties tended to have fewer doses of MenB relative to MenACWY on both public and private markets. Lower SES counties tended to have more supply of public vs. private doses. Universal purchasing programs had a strong effect on the markets for both vaccines shifting nearly all doses to the public market. School vaccination strategy was key for improving stocking rates. CONCLUSIONS: Overall, the results show that MenACWY has greater stock relative to MenB across the US. This difference is exacerbated in vulnerable areas without school entry requirements for vaccination and results in inequity of vaccine availability. Beyond state-level policy and SES differences, SCDM recommendations may be a contributing factor, although this was not directly assessed by our model.

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

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

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

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

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

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

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

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

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

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

Assessing patient perception and preferences for outcomes in lung cancer resection surgery: a cross-sectional study.

Background: Surgical resection is the primary treatment for early-stage lung cancer, but little is known about the outcomes that truly matter to patients. This aim of our study was to identify the aspects of postoperative outcomes that matter most to patients undergoing lung cancer surgery and explore the influence of clinical and demographic factors on their importance ratings. Methods: We performed a cross-sectional study of patients undergoing lung resection for non-small cell lung cancer at our institution from November 2021 to May 2022. Patients were surveyed using a self-developed questionnaire and the European Organisation for Research and Treatment of Cancer core health-related quality of life questionnaire (EORTC QLQ-C30) prior to surgery. Ordinal logistic regression was performed to determine associations between individual patient factors and outcome importance ratings. Results: Forty patients completed the survey during the study period. Patients prioritized oncologic outcomes, with 95% rating R0 resection and cancer recurrence as "very important". Other important factors included overall survival (90%), postoperative complications (e.g., myocardial infarction: 92.5%, infection: 87.5%), and the need for reoperation (82.5%). Health-related quality of life factors, such as chronic pain (77.5%) and the ability to return to normal physical and exercise levels (75%), were also highly valued. Certain patient clinical and demographic factors demonstrated significant associations with importance placed on certain outcomes. Preoperative health-related quality of life scores did not influence outcome importance ratings. Conclusions: This study provides insights into the outcomes that matter most to patients undergoing lung cancer surgery. Oncologic outcomes and postoperative complications were prioritized, while scar-related factors were less important. Patient preferences varied based on demographic and clinical factors. Understanding these preferences can enhance shared decision-making and improve patient-centered care in thoracic surgical oncology.

Predicting the number of IT staff needed in hospitals of Isfahan University of Medical Sciences based on modeling in 2023: A descriptive-analytical study.

Background and Aims: Considering the increasing use of information technology (IT) and the need of the implementation of related projects, the lack of IT specialists in the health system is one of the major challenges that require planning and foreseeing. This study was conducted with the aim of predicting the number of required IT personnel in hospitals of Isfahan University of Medical Sciences based on the modeling of identified and weighed influential factors in 2023. Method: First, Delphi method and multi-criteria decision-making (MCDM) using the Expository Posthaste Effective Resemblant Tool (ExPERT) were conducted to identify and weigh the components that affect IT staff's workload in hospitals. Then, the model for predicting the required number of IT personnel for the involved hospitals was developed. In all stages, the obtained information and results were checked and confirmed using experts' opinions in Focus Group Discussions. Results: Twenty-one hospitals (57%) out of 37 hospitals are facing a shortage of IT personnel. This varies from 0.5 to 1.6 personnel in different hospitals. Thirteen hospitals (35%) were reported to have adequate IT staffing and three hospitals (8%) had excess IT staffing. Conclusion: This study provided a predictive model for required IT staff in hospitals using MCDM through ExPERT which can be used in cases where the use of workload-based methods such as Workload Indicators of Staffing Need is complex or time-consuming.

Evaluating ChatGPT's moral competence in health care-related ethical problems.

Objectives: Artificial intelligence tools such as Chat Generative Pre-trained Transformer (ChatGPT) have been used for many health care-related applications; however, there is a lack of research on their capabilities for evaluating morally and/or ethically complex medical decisions. The objective of this study was to assess the moral competence of ChatGPT. Materials and methods: This cross-sectional study was performed between May 2023 and July 2023 using scenarios from the Moral Competence Test (MCT). Numerical responses were collected from ChatGPT 3.5 and 4.0 to assess individual and overall stage scores, including C-index and overall moral stage preference. Descriptive analysis and 2-sided Student's t-test were used for all continuous data. Results: A total of 100 iterations of the MCT were performed and moral preference was found to be higher in the latter Kohlberg-derived arguments. ChatGPT 4.0 was found to have a higher overall moral stage preference (2.325 versus 1.755) when compared to ChatGPT 3.5. ChatGPT 4.0 was also found to have a statistically higher C-index score in comparison to ChatGPT 3.5 (29.03 ± 11.10 versus 19.32 ± 10.95, P =.0000275). Discussion: ChatGPT 3.5 and 4.0 trended towards higher moral preference for the latter stages of Kohlberg's theory for both dilemmas with C-indices suggesting medium moral competence. However, both models showed moderate variation in C-index scores indicating inconsistency and further training is recommended. Conclusion: ChatGPT demonstrates medium moral competence and can evaluate arguments based on Kohlberg's theory of moral development. These findings suggest that future revisions of ChatGPT and other large language models could assist physicians in the decision-making process when encountering complex ethical scenarios.

Player selection in football by integrated SWARA-VIKOR methods under fuzzy environment.

This study aims to provide an objective evaluation and selection of the most suitable players for the Turkish National Football Team by using multi-criteria decision-making methods, specifically Stepwise Weight Assessment Ratio Analysis (SWARA) and VIsekriterijumsko KOmpromisno Rangiranje (VIKOR) based on neutrosophic numbers. In the initial step of the study, the important criteria for player selection were determined, and a total of 21 and 26 criteria were identified for goalkeeper and players, respectively. Agility, reflexes and jumping ability are very important for goalkeeper whereas marking, passing ability, agility, dribbling, and footwork are the most important criteria for players. The performances of the players were evaluated due to these criteria and finally the Turkish National Football Team was established scientifically because of the best player selection. This decision-making process will be more beneficial in making more informed and effective decisions in the national team's player selection process. The success of the team can be improved by providing an objective and systematic player selection approach.