Support Tool Research Articles

A detailed analysis of game statistics of professional tennis players: An inferential and machine learning approach.

Tennis, a widely enjoyed sport, motivates athletes and coaches to optimize training for competitive success. This retrospective predictive study examines anthropometric features and statistics of 1990 tennis players in the 2022 season, using 20,040 data points retrospectively obtained from the ATP official source after the end of the season. These data were cross-verified with information from other sources before categorisation to address any discrepancies. Employing various analytical methods, the results emphasize the strategic importance of tournament participation and gameplay for financial gains and higher rankings. Prize money analysis reveals a significant disparity favoring top players. Multivariate Analysis of Variance highlights the need to consider multiple variables for understanding ATP rankings. Multinomial Logistic Regression identifies age, height, and specific service-related metrics as key determinants, with older and taller players more likely to secure top positions. Neural Network models exhibit potential in predicting ATP Rank outcomes, particularly for ATP Rank (500). Our results argue for the use of Artificial Intelligence (AI), specifically Neural Networks, in handling complex interactions and emphasize that AI is a supportive tool in decision-making, requiring careful consideration by experienced individuals. In summary, this study enhances our understanding of ATP ranking factors, providing actionable insights for coaches, players, and stakeholders in the tennis community.

Techno-economic analysis of green hydrogen integration into existing pipeline infrastructure: A case study of Wyoming

While green hydrogen production technologies like electrolysis have reached commercial scale, the primary challenge centers on efficient, and economically viable transportation of hydrogen. This study proposes an integrated geospatial techno-economic modeling approach to optimize hydrogen transportation through blending in existing pipelines. The simulations strategically locate green hydrogen production and blending points. The paper evaluates blending ratios, flow dynamics, and operating parameters to optimize hydrogen blending in natural gas pipelines. A techno-economic analysis quantifies costs and revenues across the supply chain, identifying the most economically viable hydrogen blending locations. The approach is validated through a case study in Wyoming, providing insights for industry stakeholders considering hydrogen-natural gas blending as a transition strategy. This integrated method offers a robust decision support tool for leveraging existing infrastructure to realize hydrogen's potential as a sustainable energy vector contributing to the broader goal of cleaner energy alternatives.

THE IMPACT OF AI ON TEACHERS: SUPPORT OR REPLACEMENT?

The article explores the growing role of AI in education, analyzing whether it serves as a supportive tool for teachers or poses a threat to their jobs. AI can automate routine tasks such as grading and administrative work, freeing up teachers’ time for more meaningful activities like personalized student interaction and lesson planning. Additionally, AI-powered tools enhance data-driven teaching, providing teachers with valuable insights to improve student performance and offer individualized support. While AI offers significant advantages, the article emphasizes that it cannot replace the human qualities essential to teaching, such as emotional intelligence, empathy, and creativity. These are areas where AI falls short, making it unlikely to fully replace teachers. Instead, AI allows teachers to evolve their roles from traditional instruction to mentorship and guidance, focusing on fostering critical thinking and creativity in students. However, there are concerns that AI could reduce teaching jobs or fundamentally change the role of educators. Teachers may also resist AI due to fears of job displacement or lack of sufficient training to use these technologies effectively. The article concludes that AI, when used responsibly, acts as a valuable partner rather than a replacement. By taking over repetitive tasks, AI enables teachers to concentrate on more impactful educational activities, ensuring that the human element remains at the heart of learning.

Evaluation of the impact Of ChatGPT support on acromegaly management and patient education.

ChatGPT is a widely used artificial intelligence modeling tool. Healthcare is one potential area of use of ChatGPT. This study aimed to test the usability and reliability of ChatGPT in acromegaly, which is less known in society and should be evaluated by a group of specialized physicians. The study is designed in two parts. For the first part, 35 questions regarding acromegaly that patients frequently ask were identified, and these questions were asked to ChatGPT. In the second part, four patient examples were presented to ChatGPT using medical terminology. Three experts evaluated ChatGPT's answers to the questions and approaches in case management using 7-point scales in terms of safety, reliability, correctness, and usability. When the ChatGPT answers to the patient's questions were evaluated, a mean score of 6.78 ± 0.55 was given for correctness and 6.69 ± 0.60 for reliability. The mean scores given by the raters for correctness, safety and usability in the evaluation of the cases were as follows: 6.33 ± 0.88, 6.16 ± 0. 71 and 6.08 ± 0.79 points for case 1; 5.35 ± 1.88, 5.29 ± 1.80 and 5.20 ± 1.86 points for case 2; 6.08 ± 0.97, 6.00 ± 0.93 and 5.91 ± 0.82 points for case 3; 6.10 ± 1.29, 6.13 ± 1.30 and 6.16 ± 1.14 points for case 4. ChatGPT can actively answer the questions of acromegaly patients. Although it is not a reliable source alone in managing patients with acromegaly, it can be a supportive tool for physicians.

Teratogenic Risk Impact and Mitigation (TRIM): Study Protocol for the Development of a Decision Support Tool to Prioritize Medications for Risk Mitigation.

Preventing prenatal exposure to teratogenic medications is an important goal of regulatory risk mitigation efforts. In the USA, as of March 2024, 11 teratogenic medications have a required Risk Evaluation and Mitigation Strategy (REMS) program. It is unclear whether these programs target those medications with the most significant impact on public health and adverse pregnancy outcomes. This study aims to develop an innovative decision support tool that uses explicit, quantifiable criteria to facilitate prioritization of teratogenic medications for risk mitigation strategies. The Teratogenic Risk Impact and Mitigation (TRIM) decision support tool will be developed by a national panel via a modified Delphi approach to define measurable criteria, and a multi-criteria decision analysis to estimate criteria weights within a discrete choice experiment. The TRIM scores will then be calculated for 12 teratogenic drugs with active or eliminated REMS programs and for 12 teratogenic drugs without REMS. These drugs will be identified based on highest prenatal exposure prevalence in claims data of privately and publicly insured individuals. Data for the TRIM criteria levels for these 24 drugs will be identified from evidence searches and ad hoc analyses of the same claims data. Teratogenic Risk Impact and Mitigation is intended to inform regulatory decision making about the need for risk mitigation programs for teratogenic medications by providing explicit, quantifiable, evidence-based criteria. The TRIM scores of 24 teratogenic drugs may provide benchmarks for considering REMS for marketed and new teratogenic medications.

A multimodal deep learning-based algorithm for specific fetal heart rate events detection.

This study aims to develop a multimodal deep learning-based algorithm for detecting specific fetal heart rate (FHR) events, to enhance automatic monitoring and intelligent assessment of fetal well-being. We analyzed FHR and uterine contraction signals by combining various feature extraction techniques, including morphological features, heart rate variability features, and nonlinear domain features, with deep learning algorithms. This approach enabled us to classify four specific FHR events (bradycardia, tachycardia, acceleration, and deceleration) as well as four distinct deceleration patterns (early, late, variable, and prolonged deceleration). We proposed a multi-model deep neural network and a pre-fusion deep learning model to accurately classify the multimodal parameters derived from Cardiotocography signals. These accuracy metrics were calculated based on expert-labeled data. The algorithm achieved a classification accuracy of 96.2 % for acceleration, 94.4 % for deceleration, 90.9 % for tachycardia, and 85.8 % for bradycardia. Additionally, it achieved 67.0 % accuracy in classifying the four distinct deceleration patterns, with 80.9 % accuracy for late deceleration and 98.9 % for prolonged deceleration. The proposed multimodal deep learning algorithm serves as a reliable decision support tool for clinicians, significantly improving the detection and assessment of specific FHR events, which are crucial for fetal health monitoring.

Fostering Shared Decision-Making Between Patients and Health Care Professionals in Clinical Practice Guidelines: Protocol for a Project to Develop and Test a Tool for Guideline Developers.

Clinical practice guidelines (CPGs) are designed to assist health care professionals in medical decision-making, but they often lack effective integration of shared decision-making (SDM) principles to reflect patient values and preferences, particularly in the context of preference-sensitive CPG recommendations. To address this shortcoming and foster SDM through CPGs, the integration of patient decision aids (PDAs) into CPGs has been proposed as an important strategy. However, methods for systematically identifying and prioritizing CPG recommendations relevant to SDM and related decision support tools are currently lacking. The aim of the project is to develop (1) a tool for systematically identifying and prioritizing CPG recommendations for which SDM is considered particularly relevant and (2) a platform for PDAs to support practical SDM implementation. The project consists of 6 work packages (WPs). It is embedded in the German health care context but has an international focus. In WP 1, we will conduct a scoping review in bibliographic databases and gray literature sources to identify methods used to foster SDM via PDAs in the context of CPGs. In WP 2, we will conduct semistructured interviews with CPG experts to better understand the concepts of preference sensitivity and identify strategies for fostering SDM through CPGs. WP 3, a modified Delphi study including surveys and focus groups with SDM experts, aims to define and operationalize preference sensitivity. Based on the results of the Delphi study, we will develop a methodology for prioritizing key questions in CPGs. In WP 4, the tool will be developed. A list of relevant items to identify CPG recommendations for which SDM is most relevant will be created, tested, and iteratively refined, accompanied by the development of a user manual. In WP 5, a platform for creating and digitizing German-language PDAs will be developed to support the practical application of SDM during clinical encounters. WP 6 will conclude the project by testing the tool with newly developed and revised CPGs. The Brandenburg Medical School Ethics Committee approved the project (165122023-ANF). An international multidisciplinary advisory board is involved to guide the tool development on CPGs and SDM. Patient partners are involved throughout the project, considering the essential role of the patient perspective in SDM. As of February 20, 2024, we are currently assessing literature references to determine eligibility for inclusion in the scoping review (WP 1). We expect the project to be completed by December 31, 2026. The tool will enable CPG developers to systematically incorporate aspects of SDM into CPG development, thereby providing guideline-based support for the patient-practitioner interaction. Together, the tool for CPGs and the platform for PDAs will create a systematic link between CPGs, SDM, and PDAs, which may facilitate SDM in clinical practice. DERR1-10.2196/57611.

Corporate Social Responsibility Reports: An Analysis of Dialogicity Across Sectors and Countries

The paper explores Corporate Social Responsibility (CSR) reports in their dialogicity, i.e. as an interactive communication practice taking place between the company and a multifaceted audience of stakeholders. In particular, it investigates the linguistic and discursive resources used by a sample of companies in their CSR disclosures to articulate corporate values and performance, and voice stakeholders’ concerns. The analysis was conducted on a small collection of documents belonging to firms operating in three different sectors: Banking, Food, and Oil and Gas, and headquartered in three European countries: Italy, Switzerland and the UK. With the support of corpus linguistics tools, the CSR reports were examined in a selection of key dialogic elements and their phraseology. The quantitative and qualitative analysis was intended to map out the strategies (dis)similarly adopted by the firms across the sectors and countries to project themselves into the text, establish a connection with stakeholders, and address their needs and expectations.

КОНЦЕПТУАЛЬНІ ЗАСАДИ ФОРМУВАННЯ ІНКЛЮЗИВНОГО РИНКУ ПРАЦІ В УКРАЇНІ

Introduction. In the conditions of war, Ukraine faces numerous challenges that affect the socio-economic situation and the functioning of the labor market. At the same time, the formation of an inclusive labor market, which is able to provide equal opportunities for all citizens, including vulnerable groups of the population, which will contribute to social cohesion and economic stability of the country, becomes particularly relevant. This requires the formation of conceptual approaches to the creation of an inclusive labor market in Ukraine, taking into account the peculiarities of wartime and the challenges facing the country. The purpose of the article. The purpose of the study is to determine the conceptual foundations of the formation of an inclusive labor market in Ukraine in wartime conditions based on the identification of the main challenges for attracting vulnerable population groups to the labor market and the development of ways to overcome them in order to ensure the social and economic stability of the state. Methods. The research methodology is based on a comprehensive approach, which includes the analysis of scientific literature, regulatory and legal framework and statistical data. The research uses methods of comparative analysis to determine barriers in the labor market, a systematic approach to determine the conceptual foundations of inclusiveness, and a method of logical generalization to determine practical ways to solve the identified problems. Results. The article identifies the need for the formation of an inclusive labor market in Ukraine in the conditions of war, when socio-economic instability affects access to employment for various population groups. The main barriers to the participation of vulnerable population groups in the labor market, including veterans, internally displaced persons, people with disabilities, youth and women, have been identified. The analysis of the modern legal framework showed that there is currently a shortage of state support tools for their employment. The importance of cooperation between the state, business and the public sector to create equal employment opportunities has been proven. Conceptual approaches to the formation of an inclusive labor market are defined: implementation of professional retraining programs, development of social entrepreneurship and adaptation of workplaces. The need for the formation of a state policy aimed at ensuring equal access to the labor market for all population groups and strengthening the economic stability of Ukraine has been proven. Conclusions. The importance of the formation of an inclusive labor market in Ukraine as a key element of socio-economic stability in the conditions of war has been proven. The proposed conceptual principles ensure the expansion of opportunities for vulnerable population groups, the reduction of unemployment and the increase of social cohesion.

Role of Artificial Intelligence-Assisted Decision Support Tool for Common Rhythm Disturbances: A ChatGPT Proof-of-Concept Study

Role of Artificial Intelligence-Assisted Decision Support Tool for Common Rhythm Disturbances: A ChatGPT Proof-of-Concept Study

Motor balance and its relationship to the performance of the round kick skill (Hajo-Jki) among junior Taekwondo players

The element of balance is a very important element in the work of a Taekwondo player, as he must maintain his balance to a great extent in performance because any imbalance results in the player falling and losing attack points. In particular, motor balance, in addition to its close connection to the motor performance of the Taekwondo player, Research problem: The researcher wanted to explain the relationship between the performance of the round kick (hajo-jiki) and motor balance because this skill requires great motor balance, and for this reason he chose this kick, to gain points and thus end the competition in his favor. The study aimed to identify the relationship between the performance of the round kick skill (Hajo-Jki) among junior Taekwondo players. The researcher used the descriptive method using the survey method, and determined the research sample using the intentional method, which included Baghdad clubs, as their number reached (50(Players support tools, devices, when testing the content, preparing the results, when testing or processing the results. The researcher concluded that the results of the research demonstrated the existence of a correlation and direct relationship between motor balance and the performance of the round kick (Hago-Jki) among members of the research sample. The researcher recommended that it is necessary to conduct a survey of all the unresearched skills and apply them to other motor abilities.

Validation of a diagnostic support tool for early recognition of cervical arterial dissection in primary care

BackgroundCervical arterial dissection (CeAD) is a leading cause of stroke in young adults with an early presentation often mimicking musculoskeletal pain. Currently, no validated tests exist and CAD may be missed. A diagnostic support tool could help guide urgent referral for imaging, when to monitor, or when safe to proceed with treatment, and ultimately help stroke prevention. ObjectivesTo validate a 4-criteria diagnostic support tool for early recognition of CeAD in primary care, to refine tool descriptors as needed, and propose optimal cut-offs for clinical application. DesignProspective observational study MethodParticipants with radiologically confirmed CeAD and controls without CeAD were identified from adults >18 years presenting to a tertiary metropolitan hospital with initial diagnosis of headache or neck pain. All were scored with criteria out of7 (acute onset pain [2], recent trauma/infection [1], neurological features [2], age <55 years [2]). Diagnostic values were calculated to determine cut offs and the tool was refined based on the analysis. ResultsThirty participants with CeAD and 261 controls with non-CeAD causes of headache and neck pain were included. The diagnostic support tool was an ‘excellent’ predictor of CeAD (AUC 0.83) but demonstrated poor specificity. Refining the tool to ‘acute/sudden onset’ [1], ‘unusual/unfamiliar headache/neck pain’ [1], recent trauma/infection [1] and neurological features ≥2 [1], scored out of 4, showed 100 % sensitivity and 74 % specificity to detect CeAD at a cut-off of 3/4 (AUC 0.87). ConclusionsThe refined tool shows acceptable clinical utility at a cut-off ≥3, where referral for vascular imaging is recommended. Further validation in Emergency and primary care is needed.

Adaptation of the Taiwan Version of the Supportive and Palliative Care Indicators Tool (SPICT-TW) and Its Association with Six-Month Mortality: A Multi-Center Validation Study in Older People.

Background: The Supportive and Palliative Care Indicators Tool (SPICT) was developed for identifying, in a timely manner, patients who may benefit from supportive and palliative care for better treatment review, care-plan discussion, and end-of-life care. Although the SPICT has been validated in different languages and for patients living in different settings, it has not been validated for patients receiving home-based medical care (HBMC), or in the context of using traditional Chinese characters. Objectives: The present study aimed to validate the Taiwanese version of the SPICT (SPICT-TW) and to measure its ability to predict six-month mortality in patients who received HBMC in Taiwan. Methods: Seven HBMC agents (five clinics and two hospitals) participated in this validation study. We recruited 129 patients aged ≥ 50 years who had been consistently receiving HBMC for >two months. Results: The results revealed that the SPICT-TW demonstrated similar reliability and validity compared to other language versions of the SPICT. It may be an appropriate tool for healthcare professionals to detect, in a timely manner, the needs for palliative care in older people who receive home healthcare. Furthermore, we found that a combination of four general indicators and one clinical indicator in the SPCIT-TW has the best prediction ability at predicting six-month mortality in these HBMC recipients. This multi-center study validated the SPICT-TW among HBMC recipients in Taiwan. Conclusions: The SPICT-TW demonstrated high reliability and validity through the Kuder-Richardson 20, an intraclass correlation coefficient, Cohen's kappa, and receiver operating characteristic analysis, supporting its potential as a practical tool for identifying older adults at risk of dying within six months who have not yet received palliative care but may benefit from it.

Awareness support in collaborative programming tools: An evaluation based on programmer's perception and eye tracking

Groupware technology is an essential asset for organizations and a successful medium to support social meetings and teacher-learning processes when people are geographically distributed. Computer programming is a domain that can take advantage of the Collaborative Work paradigm and of this technology. However, to be truly effective, groupware systems must provide suitable awareness support, i.e., be capable of informing users of the activity taking place and of the team outcome, so that they can build a setting for their work. The study of awareness mechanisms and their influence on collaborative programming processes is an open research question. In this context, this article contributes an experimental study to evaluate the mechanisms to support coordination, communication, and awareness of COLLECE, a distributed group programming system rich in awareness support. This study brings as a novel approach the combination of subjective and objective information sources, specifically eye tracking techniques, and incorporates a heuristic evaluation of awareness support based on a well-known framework. Thanks to this experiment, we have been able to verify that this blended approach, in which users participate intensively, provides a more comprehensive and deeper assessment of awareness and other coordination and communication mechanisms, and thus a better understanding of how their use can influence the collaborative programming process and outcome (program or piece of software). The study has revealed strengths, weaknesses and opportunities for improvement of the evaluated system, and has yielded concrete results as to which are the most useful awareness components of the user interface not related to the activity supported by the system (programming), these being the traffic lights; the easiest to use, such as the session panel; or the components that impose a greater cognitive load, such as the multi-scrollbar. Regarding the relationship between the supported awareness dimensions and the quality of the collaboration outcome (computer program), the results point to the need to adequately support the What-Artifact dimension, i.e., highlighting in which part of the shared workspace (the source code) the user with floor control is working.

Decision support tools for effective bus fleet electrification: Replacement factors and fleet size prediction

The electrification of public transit systems represents a crucial strategy for advancing sustainable urban mobility. Thus, the development of efficient charging infrastructure and the optimization of fleet size emerge as major challenges for transit agencies. Switching from diesel buses to electric buses (Ebuses) will require increasing the fleet size to accommodate the limited range of Ebuses and the significant idle time required for charging. This study develops prediction models to estimate the required Ebus fleet size to maintain same transit route services for the case of overnight depot charging, using data from Ebuses operating in the City of Toronto. The analysis reveals that Ebuses equipped with diesel auxiliary heaters are less sensitive to temperature fluctuations compared to battery-heated buses. Thus, the required replacement factor, indicating the additional fleet needed to switch from diesel to Ebuses, varies depending on the heating system. Specifically, diesel-heated buses require a lower replacement factor (1.3) compared to battery-heated buses (1.4), with winter conditions exacerbating this disparity. Furthermore, the study employs vehicular, operational, route, and external variables to develop the prediction models. Additionally, SHAP analysis is utilized to interpret the machine learning models and evaluate the influence of the inputs on the required fleet size. The results show that the total distance traveled, and the average temperature are the primary factors affecting the fleet size for Ebuses using their batteries for heating, whereas the total distance traveled, and the average bus speed are the primary factors affecting the fleet size for Ebuses with diesel auxiliary heaters.

Cost-effectiveness of clinical decision support to improve CKD outcomes among First Nations Australians

IntroductionThe Northern Territory (NT) is a hotspot for chronic kidney disease (CKD) and has a high incidence of kidney replacement therapy (KRT). The Territory Kidney Care clinical decision support tool (CDS) aims to improve diagnosis and management of CKD in remote NT, particularly amongst First Nations Australians. We model the cost-effectiveness of the CDS versus usual care. MethodsTaking a healthcare funder perspective, we modelled a cohort of people from remote NT at risk of or with CKD, as of 1 January 2017. A Markov cohort model was developed using 6 years of observed patient-level data (2017 to 2023), extrapolated to a 15-year time horizon. The CDS tool was modelled to improve CKD diagnosis (scenario 1), improve management (scenario 2), or improve both diagnosis and management (scenario 3). ResultsThe remote NT cohort consisted of 23,195 people, predominantly (89%) First Nations, with a mean age of 42. Scenario 3 (improved diagnosis and management) was most cost-effective at an incremental cost-effectiveness ratio (ICER) of $96,684 per patient avoiding KRT, $30,086 per patient avoiding death. Scenario 1 (improved diagnosis) was less cost-effective, and scenario 2 (improved management) was least cost-effective. The ICER per quality adjusted life years gained ranged from $3,427 (scenario 3) to $63,486 (scenario 2). ConclusionTerritory Kidney Care is highly cost-effective when it supports early diagnosis of CKD and increases optimal management in diagnosed patients. These results support investing in CDS tools, implemented in strong partnerships, to improve outcomes in settings with a high burden of CKD.

Optimized Deep Learning Model for Medical Image Diagnosis

Deep learning models, particularly convolutional neural networks (CNNs), have excelled in pattern recognition tasks like face recognition, pedestrian detection, and medical diagnosis. CNNs, known for their end-to-end feature extraction and classification, are widely used in computer vision. Pre-trained CNN models such as AlexNet, VGG-19, ResNet, and Inception can serve as feature extractors for various problems, though they often produce a large number of features. To address this challenge and reduce the number of features, this research employs an efficient two-layer feature selection algorithm. The first layer, a global search optimizer, guides the swarm in the search space to identify the optimal feature subset. The second layer refines and fine-tunes the solution obtained from the global search. The main contributions of this work encompass several aspects: (i) the introduction of a lightweight classification model that utilizes a subset of features, (ii) the use of an efficient optimizer to perform simultaneous feature and instance selection, and (iii) the direct implications for the radiology field, where it could serve as a supportive tool for diagnosis. To evaluate the proposed approach, it was tested on skin lesion diagnosis using 1113 abnormal and 1099 normal lesions. The pre-trained VGG-19 model provided a 1-D feature vector of size 4096, with a linear SVM used for classification. Results showed the optimizer reduced the feature vector by up to 86% while achieving over 70% accuracy. The proposed optimizer outperformed the Reinforcement Learning-based Memetic Particle Swarm Optimization (RLMPSO).

Widespread Adoption of Precision Anticancer Therapies After Implementation of Pathologist-Directed Comprehensive Genomic Profiling Across a Large US Health System.

Precision therapies and immunotherapies have revolutionized cancer care, with novel genomic biomarker-associated therapies being introduced into clinical practice rapidly, resulting in notable gains in patient survival. Despite this, there is significant variability in the utilization of tumor molecular profiling that spans the timing of test ordering, comprehensiveness of gene panels, and clinical decision support through therapy and trial recommendations. To standardize testing, we designed a pathologist-directed test ordering system at the time of diagnosis using a 523-gene DNA/RNA hybrid comprehensive genomic profiling (CGP) panel and extensive clinical decision support tools. To comprehensively characterize the clinical impact of this protocol, we developed a novel natural language processing (NLP)-based approach to extract clinical features from physician chart notes. We assessed test actionability rates, therapy choice, and outcomes across a set of 3,216 patients with advanced cancer. We observed 49% of patients had at least one actionable genomic biomarker-driven-approved and/or guideline-recommended targeted or immunotherapy (IO) and 53% of patients would have been eligible for a precision therapy clinical trial from three large basket trials. When assessing CGP versus an in silico 50-gene panel, 67% of tumors compared with 33% harbored actionable alterations including clinical trials. Among patients with 6 months or more of follow-up, over 52% received a targeted therapy (TT) or IO, versus 32% who received conventional chemotherapy alone. Furthermore, patients receiving TT had significantly improved overall survival compared with patients receiving chemotherapy alone (P < .001). Overall, these data represent a major shift in standard clinical practice toward molecularly guided treatments (targeted and immunotherapies) over conventional systemic chemotherapy. As guidelines continue to evolve and more precision therapeutics gain approval, we expect this gap to continue to widen.

Creating Perinatal Nursing Care Plans Using ChatGPT: A Pathway to Improve Nursing Care Plans and Reduce Documentation Burden.

Extensive time spent on documentation in electronic health records (EHRs) impedes patient care and contributes to nurse burnout. Artificial intelligence-based clinical decision support tools within the EHR, such as ChatGPT, can provide care plan recommendations to the perinatal nurse. The lack of explicit methodologies for effectively integrating ChatGPT led to our initiative to build and demonstrate our ChatGPT-4 prompt to support nurse care planning. We employed our process model, previously tested with 22 diverse medical-surgical patient scenarios, to generate a tailored prompt for ChatGPT-4 to produce care plan suggestions for an exemplar patient presenting with preterm labor and gestational diabetes. A comparative analysis was conducted by evaluating the output against a "nurse-generated care plan" developed by our team of nurses on content alignment, accuracy of standardized nursing terminology, and prioritization of care. ChatGPT-4 delivered suggestions for nursing diagnoses, interventions, and outcomes comparable to the "nurse-generated care plan." It accurately identified major care areas, avoided irrelevant or unnecessary recommendations, and identified top priority care. Of the 24 labels generated by ChatGPT-4, 16 correctly utilized standardized nursing terminology. This demonstration of the use of our ChatGPT-4 prompt illustrates the potential of leveraging a large language model to assist perinatal nurses in creating care plans. The next steps are improving the accuracy of ChatGPT-4-generated standardized nursing terminology and integrating our prompt into EHRs. This work supports our broader goal of enhancing patient outcomes while mitigating the burden of documentation that contributes to nurse burnout.

Reviewability and supportability: New complementary principles to empower research software practices

In today's scientific landscape, research software has evolved from being a supportive tool to becoming a fundamental driver of discovery, particularly in life sciences. Beyond its roots in software engineering, research software now plays a crucial role in facilitating efficient data analysis and enabling the exploration of complex natural phenomena. The advancements in simulations and modeling through research software have significantly accelerated the pace of scientific research while reducing associated costs. This growing reliance underscores the importance of software in ensuring reproducibility – a cornerstone of scientific rigor and trustworthiness. Although verifying reproducibility presents challenges, well-developed and openly accessible research software enhances transparency and aids in the early detection of errors. Although verifying reproducibility can be challenging, well-developed and accessible research software improves transparency and facilitates error detection. This mini-review examines the characteristics of research software and summarizes the key events that have shaped its development, alongside changes in requirements and guidelines. Moreover, we propose two additional principles – reviewability and supportability – complementing the widely accepted FAIR principles (Findability, Accessibility, Interoperability, and Reusability). These new principles aim to improve the efficiency and effectiveness of software evaluation during the peer review process. Through this review, we aim to assist scientists, especially those without extensive software development expertise, in understanding best practices for developing research software and the underlying motivations driving these practices.