Important Decisions Research Articles

Precipitation and temperature timings underlying bioclimatic variables rearrange under climate change globally.

Modeling how climate change may affect the potential distribution of species and communities typically utilizes bioclimatic variables. Distribution predictions rely on the values of the bioclimatic variable (e.g., precipitation of the wettest quarter). However, the ecological meaning of most of these variables depends strongly on the within-year position of a specific climate period (SCP), for example, the wettest quarter of the year, which is often overlooked. Our aim was to determine how the within-year position of the SCPs would shift (SCP shift) in reaction to climate change in a global context. We calculated the deviations of the future within-year position of the SCPs relative to the reference period. We used four future time periods, four scenarios, and four CMIP6 global climate models (GCMs) to provide an ensemble of expectations regarding SCP shifts and locate the spatial hotspots of the shifts. Also, the size and frequency of the SCP shifts were subjected to linear models to evaluate the importance of the impact modeler's decision on time period, scenario, and GCM. We found ample examples of SCP shifts exceeding 2 months, with 6-month shifts being predicted as well. Many areas in the tropics are expected to experience both temperature and precipitation-related shifts, but precipitation-related shifts are abundantly predicted for the temperate and arctic zones as well. The combined shifts at the Equator reinforce the likelihood of the emergence of no-analogue climates there. The shifts become more pronounced as time and scenario progress, while GCMs could not be ranked in a clear order in this respect. For most SCPs, the modeler's decision on the GCM was the least important, while the choice of time period was typically more important than the choice of scenario. Future predictive distribution models should account for SCP shifts and incorporate the phenomenon in the modeling efforts.

EDNA State and Medium Affect DNA Degradation Patterns in Seminatural Systems of Southern African Waterholes

ABSTRACTEnvironmental DNA (eDNA) has evolved into a valuable asset of the ecologists' toolkit, enabling time‐ and cost‐efficient biodiversity assessments in a wide variety of ecosystems. Since eDNA can be isolated from a broad range of environmental substrates, its persistence times in those media are of decisive importance for drawing inferences about species presence. For the first time, we characterize eDNA persistence in water and sediment samples of seminatural waterholes in a savanna system in South Africa to gain a better understanding of eDNA decay in these waterbodies. Using mesocosm experiments, we tracked eDNA decay in two different DNA states (extracellular and membrane bound), during two different seasons (wet and dry), and from two different substrates (surface water and sediment). Extracellular DNA degraded rapidly in a first‐order exponential decay fashion and membrane‐bound DNA exhibited a slower decline with more intricate patterns, involving initial reduction followed by a subsequent increase in measured DNA concentrations. The latter we attribute to cell disassociation and cell lysis at 24–48 h after introduction into the environment. Higher stochasticity of membrane‐bound DNA capture in the dry season highlights the need for higher sampling efforts in natural systems in which eDNA is presumably more patchily distributed. Additionally, we observed longer eDNA persistence in sediments than in water samples, presumably due to better protection from nucleases. We could not reveal any effects of environmental parameters on eDNA decay, emphasizing that further research is needed to better understand eDNA dynamics in those waterbodies in order to exploit their full potential for eDNA‐based bioassessments in those systems.

Participation of Students in all Areas of Governance in Higher Education Institutions in the Light of the Literature – A Systematic Review

This paper presents a comprehensive methodology for conducting a systematic literature review on student participation in higher education institutions (HEIs), focusing on decision-making, student engagement, and extracurricular activities. Following PRISMA guidelines, the review uses evaluation criteria techniques to ensure the quality and transparency of information critical to the design of the paper. A search strategy utilising key terms across EBSCO and Web of Science databases identified 41 relevant articles. Each article underwent thorough analysis, addressing research inquiries about student participation in educational institutions. The review combines findings from diverse perspectives while highlighting students’ importance in decision-making and the relevance of student voices in this process. In addition, it highlights the importance of student engagement and the impact of extracurricular activities on academic success. Through such discussions, the paper emphasises the need for integrated frameworks focused on understanding student participation across higher education institutions, suggesting recommendations for optimising student involvement or participation in decision-making processes, fostering student engagement in teaching and learning, and enhancing participation in extracurricular activities. Overall, the paper highlights the multi-dynamic nature of student participation in the modern structure of higher education institutions and advocates for a holistic approach that empowers and motivates students to participate in all areas of the education system, which will go a long way in shaping their educational experiences.

Fire safety status and evacuation of medical facility considering elevated oxygen concentrations

The prevalence of infectious diseases and rapid population expansion has increased the number of medical facilities. Due to the patients' limited mobility, these hospitals are more susceptible to fire disasters. Both Pathfinder and Fire Dynamic simulators were used to calculate the required safe evacuation time (RSET), heat release rate, visibility, temperature, CO, and oxygen effects on temperature, and available safe evacuation time (ASET). The safety egress of the medical hospital was then evaluated by comparing the available safe evacuation time (ASET) and required safe evacuation time (RSET). The simulation findings showed that the egress guides depend on delay time when delay time increases the egress guides drop. We also studied the importance of egress route decisions and suggested that at least 20 to 30 egress guides be on duty in the medical facility. The safety criteria for the medical facility have been proposed based on the delay time with the normalized egress guides. The high oxygen concentration in a hospital can put the medical staff and patients in danger and limit the required safe egress time to less than 150s. The proposed measures can be used to assess the evacuation safety of a typical medical hospital in use relatively quickly and efficiently.

Implementasi Total Quality Management dalam Meningkatkan Daya Saing Perusahaan

This research examines the effect of implementing Total Quality Management (TQM) on company competitiveness. The results show that TQM has a significant and positive effect on increasing competitiveness and company performance. Key contributing factors include customer focus, continuous system improvement, education and training, and employee involvement and empowerment. TQM improves various aspects of competitiveness such as operational efficiency, innovation, customer satisfaction, productivity and product quality. Effective strategies to overcome TQM implementation challenges include top management commitment, development of a quality culture, gradual implementation, effective communication, use of quality tools, continuous evaluation, and encouraging innovation. The research also discusses the impact of TQM on supply chain management and the importance of data-based decision making. In conclusion, effective TQM implementation can significantly increase a company's competitiveness in an increasingly competitive global market.

Numerical modelling of global/local mechanisms and sensitivity analysis for the seismic vulnerability assessment of glass curtain walls

Façades represent complex and vulnerable components for buildings, due to the intrinsic fragility and brittleness of glass, and require specific design and calculation tools, both for ordinary and extreme design loads. In this context, the availability of consolidated and strategic modelling strategies is of utmost importance for structural safety considerations and optimal decisions. In this paper, the attention is focused on the assessment of different modelling strategies for the global and local behaviour analysis of Glass Curtain Walls (GCWs) under in-plane seismic loads. Benefits and limitations are discussed for a more refined (but expensive, “MREF”) and a simplified (and computationally efficient, but weak in capturing local phenomena, “MSIMP”) model assemblies developed in ABAQUS and SAP2000, respectively. Careful consideration is given for typical mechanisms, local responses, key performance indicators for the façade components (i.e., stress and deformation peaks) for a reference GCW under monotonic and cyclic loads, as well as for major effects due to general input parameters (i.e., contact mechanisms, clearance, friction, bare frame features, secondary components, etc.), based on a parametric sensitivity investigation. The parametric comparative results are critically discussed, with the support of an experimental prototype test from literature, and the most influencing parameters are emphasized, to improve and facilitate the development of further design and modelling optimization strategies.

Prognostic factors for thyroid-stimulating immunoglobulin normalization in moderate-to-severe Graves’ orbitopathy: a 36-month longitudinal study

PurposeThe primary objective of this study was to identify predictive factors linked to the normalization of thyroid-stimulating immunoglobulin (TSI) levels in patients diagnosed with active, moderate-to-severe Graves’ orbitopathy (GO). The study also tracked the longitudinal changes in TSI levels over a 36-month period following treatment.MethodsThe study population consisted of individuals who were recently diagnosed with active, moderate-to-severe GO and received a 12-week course of intravenous methylprednisolone (IVMP) treatment. A subgroup of patients who did not respond to the initial treatment received an additional 20 Gy of radiation therapy (RTx). TSI levels were monitored at the time of diagnosis, after treatment, and subsequently every 6 months for 36 months. Normalization was defined as a TSI level below 140%. Patients were divdied into two groups with success and failure group depending on whether TSI became normal or not.ResultsOut of 83 patients, 36 (43.4%) achieved normalized TSI levels within two years post-IVMP treatment. Lower initial TSI levels (< 425%), absence of additional RTx, and early treatment initiation were associated with a higher likelihood of TSI normalization (P = 0.035, P = 0.028, P < 0.001, respectively). Notably, significant differences in TSI level reduction were observed from 18 months post-treatment between the two groups (P = 0.031). A TSI cutoff value of 413% was identified as predictive for normalization at 24 months (P = 0.002).ConclusionThis study is the first to identify key factors that influence normalization of TSI levels in moderate-to-severe Graves’ Orbitopathy. It highlights the importance of early treatment decisions, particularly for patients with initial TSI levels above 425%. Despite the treatment, less than half of the patients achieved TSI normalization within 24 months, underscoring the need for additional research to explore the relationship between TSI levels and the clinical manifestations of chronic GO.

ENTERPRISE MANAGEMENT IN DIGITIZATION CONDITIONS — THE FEEDABILITY OF IMPLEMENTING DIGITAL SOLUTIONS IN THE MIRROR OF STATISTICS

Digitalization of the economy is a complex phenomenon, on the one hand, aimed at increasing the innovation and competitiveness of industries, but on the other, requiring significant resources. And the benefits of implementing a digital transformation policy for specific enterprises do not always outweigh the resource intensity or negative aspects of the process. Therefore, studying the effectiveness of digitalization in the context of individual enterprises, industries and regions is of decisive importance for planning expenses for these segments of activity. The current work is devoted to the analysis of statistical surveys of enterprises for 2022 on the effectiveness of implementing digital solutions in their work, both in terms of economic effect and practical benefits in other aspects of the company’s activities.

Contextual considerations in TPACK: Collaborative processes in initial teacher education

The importance of educational technologies continues to be revealed in contemporary classrooms and has consequently resulted in calls for those in initial teacher education programs to engage with contexts in which they can develop and demonstrate their technological, pedagogical and content knowledge (TPACK). Despite TPACK being one of the world's most frequently used frameworks for understanding teachers’ technology integration (Hew et al., 2019; Niederhauser & Lindström, 2018), conceptualisations of the contexts in which pre-service teachers develop and demonstrate their TPACK are often confined to physical locations (Phillips et al., 2016; Phillips, 2016b; Rosenberg & Koehler, 2015). This research takes a broader consideration of ‘context’ through explorations of empirical data from a study of Australian pre-service teachers (PSTs) collaboratively planning, delivering and reflecting on team-taught, online, high school STEM classes in an attempt to answer the research question driving this research: What contextually situated learning process shape pre-service teachers’ TPACK development? Data from a series of semi-structured interviews revealed changes in the ways in which pre-service teachers conceptualised their TPACK. More importantly, this study found that the situated learning processes of induction, transferability, positive interdependence, synchronicity, and negotiability (Johnson and Johnson, 1994; Dillenbourg, 1999) were particularly important in explaining how participants understood and explained their TPACK acquisition and enactment. This research also highlights the importance of intentional design decisions by teacher educators to consider broader conceptualisations of ‘contexts’ which illustrate the importance of situated and collaborative learning opportunities to shape pre-service teachers TPACK. Importantly, these designs in this setting also empowered pre-service teachers to take risks with their praxis as they reported a feeling of emotional safety when teaching with a liminal peer. Consequently, the power of collaborative processes and interactions were revealed as new contextual considerations in the TPACK framework.

Treatment Preferences of Adult Patients with Attention-Deficit/Hyperactivity Disorder - A Discrete Choice Experiment.

Understanding patient preferences for treatments may facilitate shared decision-making. This study assessed adult patient preferences for attention-deficit/hyperactivity disorder (ADHD) treatments in a sample of 600 patients in the United States (US). A web-based discrete choice experiment (DCE) survey was conducted among treated adults with ADHD. Participants were recruited from Dynata's US panel (06/22/2023-07/06/2023). Attributes and levels, identified based on clinical inputs and published data, included efficacy and safety. Participants' preferences were estimated using conditional logistic regression. Willingness to trade-off and attributes' relative importance were calculated. Overall preferences for treatment profiles approximating centanafadine, lisdexamfetamine, atomoxetine, and viloxazine were estimated using adjusted total utilities. Results were stratified by current treatment status. Sensitivity analyses including participants who passed validity tests were conducted. Among the 600 participants (mean age 37.9 years; 66.2% female; 50.8% treated), all attributes had a statistically significant impact on preferences for ADHD treatments (p < 0.001); the most important attribute was improvement in ADHD symptoms (36%), followed by risks of nausea (25%), insomnia (20%), anxiety (8%), dry mouth (6%), and feeling jittery (5%). Together, safety attributes accounted for >60% of relative importance in decision-making. Participants were willing to forgo 0.59, 0.57, 0.49, 0.32, and 0.17 percentage points of symptom improvement to achieve one-percentage-point reduced risk of insomnia, nausea, anxiety, feeling jittery, and dry mouth, respectively. Centanafadine profile had consistently higher adjusted total utilities than its comparators. Similar results were obtained in the subgroup and sensitivity analyses. Efficacy was the most important attribute for patients when making treatment decision, but taken together, AEs had greater relative importance than efficacy alone. Accordingly, a profile resembling that of centanafadine would be preferred by an average patient compared to key competitors due to its favorable safety profile. These findings may help improve treatment decision-making, enhance treatment satisfaction, and foster adherence.

The survival outcomes of localized low-risk prostate cancer, a population-based study using NCDB.

The optimal treatment approach for low-risk prostate cancer (LRPC) remains controversial. While active surveillance is an increasingly popular option, definitive local treatments, including radical prostatectomy (RP), external beam radiotherapy (EBRT), and prostate seed implantation (PSI), are also commonly used. This study aimed to evaluate the survival outcomes of patients with LRPC using a large patient population from the National Cancer Database (NCDB). We analyzed data from 195,452 patients diagnosed with LRPC between 2004 and 2015 using the NCDB. Patients were classified based on their treatment modalities, including RP, EBRT, PSI, or no local treatment (NLT). Only patients with Charlson-Deyo comorbidity scores of 0 or 1 were included to ensure comparability. Propensity score analysis was used to balance the treatment groups, and the accelerated failure time model was used to analyze the survival rates of the treatment groups. After a median follow-up of 70.8 months, 24,545 deaths occurred, resulting in an all-cause mortality rate of 13%. RP demonstrated a survival benefit compared with NLT, particularly in patients younger than 74 years of age. In contrast, radiation treatments (EBRT and PSI) did not improve survival in the younger age groups, except for patients older than 70 years for EBRT and older than 65 years for PSI. Notably, EBRT in patients younger than 65 years was associated with inferior outcomes. This study highlights the differences in survival outcomes among LRPC treatment modalities. RP was associated with improved survival compared to NLT, especially in younger patients. In contrast, EBRT and PSI showed survival benefits primarily in the older age groups. NLT is a reasonable choice, particularly in younger patients when RP is not chosen. These findings emphasize the importance of individualized treatment decisions for LRPC management.

Enhancing construction organizations’ performance through strategic decision-making: unveiling the mediating role of quality of information

Purpose Formulating strategic decisions poses a significant challenge for construction organizations, profoundly impacting their overarching strategic management. The success of an organization’s strategy relies on how information is managed and decisions are executed. However, the literature has a limited understanding of the connection between information quality and strategic decision-making, particularly in construction business performance. This study aims to bridge this gap by exploring how information quality mediates the relationship between strategic decision-making and the performance of construction businesses in New Zealand. Design/methodology/approach This quantitative study aims to fill this gap by assessing how information quality shapes strategic decision-making practices, impacting construction organizations’ performance. Analysing 102 viable responses through partial least squares structural equation modeling structural equation modelling offers partial support to the research framework. Findings The study used statistical analysis to gauge the impact of adopting strategic management practices on construction business performance, considering the mediation of the quality of information within New Zealand’s context. It affirmed a positive correlation between strategic decision-making management and construction business performance, underpinned by the mediation of quality of information. Practical implications This study underscores the critical role of information quality in evaluating strategic decisions for bolstering construction business performance. In essence, it affirms that enhancing the performance of construction organizations via strategic decision-making is intrinsically linked to the quality of information. Originality/value This study makes a noteworthy contribution by establishing connections between decision importance, process effectiveness, information quality, intuition in decision-making and model development, providing valuable insights to the field.

More on the scoring of Josquin’s Huc me sydereo and the manuscript St Gallen 464

Abstract The motet Huc me sydereo by Josquin des Prez appears in both five- and six-voice versions, having led scholars to debate which version is authentic to the composer’s original conception. The authoritative New Josquin Edition presents the motet in its six-voice version; in the critical commentary, editor Bonnie Blackburn defended it as Josquin’s original. More recently, Joshua Rifkin has demonstrated on stylistic and source-critical grounds that the five-voice version was original, with a sixth voice having been added later. In this article, I present new findings on the manuscript St Gallen 464, an important early source for Huc me sydereo, which reinforce arguments defending the authenticity of the five-voice version. I show that the origin of the sixth voice can be isolated to a trio of sources produced on the Italian peninsula in the 1510s, all but eliminating Josquin as a candidate as its author. Moreover, through a new identification of the original scribe and owner of St Gallen 464, I shed new light on that manuscript’s date and provenance. In so doing, I demonstrate how the study of musical sources and transmission can carry direct importance for essential performance decisions.

CYSTATIN C-A Monitoring Perspective of Chronic Kidney Disease in Patients with Diabetes.

Chronic kidney disease (CKD) is a microvascular complication that frequently affects numerous patients diagnosed with diabetes. For the diagnosis of CKD, the guidelines recommend the identification of the urinary albumin/creatinine ratio and the determination of serum creatinine, based on which the estimated rate of glomerular filtration (eGFR) is calculated. Serum creatinine is routinely measured in clinical practice and reported as creatinine-based estimated glomerular filtration rate (eGFRcr). It has enormous importance in numerous clinical decisions, including the detection and management of CKD, the interpretation of symptoms potentially related to this pathology and the determination of drug dosage. The equations based on cystatin C involve smaller differences between race groups compared to GFR estimates based solely on creatinine. The cystatin C-based estimated glomerular filtration rate (eGFRcys) or its combination with creatinine (eGFRcr-cys) are suggested as confirmatory tests in cases where creatinine is known to be less precise or where a more valid GFR estimate is necessary for medical decisions. Serum creatinine is influenced by numerous factors: age, gender, race, muscle mass, high-protein diet, including protein supplements, and the use of medications that decrease tubular creatinine excretion (H2 blockers, trimethoprim, fenofibrate, ritonavir, and other HIV drugs). The low levels of creatinine stemming from a vegetarian diet, limb amputation, and conditions associated with sarcopenia such as cirrhosis, malnutrition, and malignancies may lead to inaccurately lower eGFRcr values. Therefore, determining the GFR based on serum creatinine is not very precise. This review aims to identify a new perspective in monitoring renal function, considering the disadvantages of determining the GFR based exclusively on serum creatinine.

WILDPOP: AN INTERACTIVE TOOL FOR ESTIMATING OCCUPANCY AND ABUNDANCE OF WILDLIFE POPULATIONS

Species abundance or population size is an ecological parameter of critical importance for wildlife management and conservation decisions. Widely used data collection methods, such as sign surveys and remote cameras, often count non-identifiable individuals or individuals prone to misidentification. In ecological modeling, these individuals are considered unmarked, and a state-of-the-art modeling approach for such data is occupancy-type modeling for unmarked individuals, which explicitly incorporates imperfect detection. Hierarchical modeling of this kind requires advanced statistical analyses, typically conducted using the R software platform and the “unmarked” package. However, these models can be explored only by researchers with programming skills and a thorough understanding of hierarchical analysis of wildlife population data. To help researchers and practitioners implement these models, we have developed a Shiny web-based interactive tool for wildlife population assessment, which works with data collected by scientists and wildlife managers. This app facilitates the use of occupancy-type modeling for unmarked individuals (single-season single species occupancy and N-mixture models) for non-coder users. The app performs simulations for single-season single species occupancy and N-mixture models with or without covariates, and estimates occupancy and abundance employing users provided data. The results are displayed as text, tables, and graphs, helping users understand hierarchical modeling and answering real-life wildlife management questions.

Calibration of Microscopic Traffic Simulation in an Urban Environment Using GPS-Data

Accurate traffic models are of decisive importance for well-founded traffic engineering and represent the basic framework for comprehensive simulation studies as modelling of traffic demand. Using traffic count and speed measurements of road segments is a common approach for the calibration of a realistic traffic simulation although the data acquisition process can be at very extensive costs. From an academical point of view, there have been many studies addressing the problem of calibration. In this respect, the microscopic simulation software SUMO offers the usage of the tools flowrouter and routesampler for generating network simulations on the base of traffic count measurements. In this paper, we propose a robust method for the calibration of microscopic traffic simulations by using vehicle count and speed measurements from collected GPS-data. The developed approach is a two-step optimization process: The application of integer linear programming (ILP) as a priori optimization is followed by adopting an evolutionary algorithm for minimizing the a posteriori deviation between real and simulated traffic data. As a proof of concept, the proposed method is tested in a subnet-work model of the inner city of Friedrichshafen and compared with the ready-to-use tools from SUMO. The suggested method indicates a promising correlation between simulated and real traffic data showing better calibration results in comparison to the aforementioned functions SUMO provides. Since the approach is network-independent, it also offers the possibility of large-scale traffic calibration.

Explainable machine learning on baseline MRI predicts multiple sclerosis trajectory descriptors.

Multiple sclerosis (MS) is a multifaceted neurological condition characterized by challenges in timely diagnosis and personalized patient management. The application of Artificial Intelligence (AI) to MS holds promises for early detection, accurate diagnosis, and predictive modeling. The objectives of this study are: 1) to propose new MS trajectory descriptors that could be employed in Machine Learning (ML) regressors and classifiers to predict patient evolution; 2) to explore the contribution of ML models in discerning MS trajectory descriptors using only baseline Magnetic Resonance Imaging (MRI) studies. This study involved 446 MS patients who had a baseline MRI, at least two measurements of Expanded Disability Status Scale (EDSS), and a 1-year follow-up. Patients were divided into two groups: 1) for model development and 2) for evaluation. Three descriptors: β1, β2, and EDSS(t), were related to baseline MRI parameters using regression and classification XGBoost models. Shapley Additive Explanations (SHAP) analysis enhanced model transparency by identifying influential features. The results of this study demonstrate the potential of AI in predicting MS progression using the proposed patient trajectories and baseline MRI scans, outperforming classic Multiple Linear Regression (MLR) methods. In conclusion, MS trajectory descriptors are crucial; incorporating AI analysis into MRI assessments presents promising opportunities to advance predictive capabilities. SHAP analysis enhances model interpretation, revealing feature importance for clinical decisions.

Mechanistic Insights of Neuroprotective Efficacy of Verapamil-Loaded Carbon Quantum Dots against LPS-Induced Neurotoxicity in Rats.

Alzheimer's disease (AD) is a neurodegenerative disease that badly impacts patients and their caregivers. AD is characterized by deposition of amyloid beta (Aβ) and phosphorylated tau protein (pTau) in the brain with underlying neuroinflammation. We aimed to develop a neuroprotective paradigm by loading verapamil (VRH) into hyaluronic acid-modified carbon quantum dots (CQDs) and comparing its effectiveness with the free form in an AD-like model in rats induced by lipopolysaccharide (LPS). The experimental rats were divided into seven groups: control, LPS, CQDs, early free VRH (FVRH), late FVRH, early verapamil carbon quantum dots (VCQDs), and late VCQDs. Characterizations of VCQDs, the behavioral performance of the rats, histopathological and immunohistochemical changes, some AD hallmarks, oxidative stress biomarkers, neuro-affecting genes, and DNA fragmentation were determined. VRH was successfully loaded into CQDs, which was confirmed by the measured parameters. VRH showed enhancement in cognitive functions, disruption to the architecture of the brain, decreased Aβ and pTau, increased antioxidant capacity, modifiable expression of genes, and a decline in DNA fragmentation. The loaded therapy was superior to the free drug. Moreover, the early intervention was better than the late, confirming the implication of the detected molecular targets in the development of AD. VRH showed multifaceted mechanisms in combating LPS-induced neurotoxicity through its anti-inflammatory and antioxidant properties, thereby mitigating the hallmarks of AD. Additionally, the synthesized nanosystem approach exhibited superior neuroprotection owing to the advantages offered by CQDs. However, finding new actionable biomarkers and molecular targets is of decisive importance to improve the outcomes for patients with AD.

Aspect-based drug review classification through a hybrid model with ant colony optimization using deep learning

The task of aspect-level sentiment analysis is intricately designed to determine the sentiment polarity directed towards a specific target within a sentence. With the increasing availability of online reviews and the growing importance of healthcare decisions, analyzing drug reviews has become a critical task. Traditional sentiment analysis, which categorizes a whole review as positive, negative, or neutral, provides limited insights for consumers and healthcare professionals. Aspect-based sentiment analysis (ABSA) aims to overcome these limitations by identifying and evaluating the sentiment associated with specific aspects or attributes of drugs mentioned in the reviews. Various fields, including business, politics, and medicine, have been explored in the context of sentiment analysis. Automation of online user reviews allows pharmaceutical companies to assess large amounts of user feedback. This helps extract pharmacological efficacy and side effect insights. The data collected could improve pharmacovigilance. Reviewing user comments can provide valuable data that can be used to improve drug safety and efficacy monitoring procedures. This improves pharmacovigilance processes, improving pharmaceutical outcomes understanding and corporate decision-making. Therefore, we propose a pre-trained RoBERTa with a Bi-LSTM model to categorise drug reviews from online sources and pre-process the text data. Ant Colony Optimization can be used in feature selection for ABSA, helping to identify the most relevant aspects and sentiments. Further, RoBERTa is fine-tuned to perform ABSA on the dataset, enabling the system to categorize aspects and determine the associated sentiment. The outcomes reveal that the suggested framework has achieved higher accuracy (96.78%) and F1 score (98.29%) on druglib.com, and 95.02% on the drugs.com dataset, than several prior state-of-the-art methods.

A taxonomy of demand-driven questions for use by evidence producers, intermediaries and decision-makers: results from a cross-sectional survey

BackgroundGlobally, a growing number of calls to formalize and strengthen evidence-support systems have been released, all of which emphasize the importance of evidence-informed decision making. To achieve this, it is critical that evidence producers and decision-makers interact, and that decision-makers’ evidence needs can be efficiently translated into questions to which evidence producers can respond. This paper aims to create a taxonomy of demand-driven questions for use by evidence producers, intermediaries (i.e., people working in between researchers and decision-makers) and decision-makers.MethodsWe conducted a global cross-sectional survey of units providing some type of evidence support at the explicit request of decision-makers. Unit representatives were invited to answer an online questionnaire where they were asked to provide a list of the questions that they have addressed through their evidence-support mechanism. Descriptive analyses were used to analyze the survey responses, while the questions collected from each unit were iteratively analyzed to create a mutually exclusive and collectively exhaustive list of types of questions that can be answered with some form of evidence.ResultsTwenty-nine individuals completed the questionnaire, and more than 250 submitted questions were analysed to create a taxonomy of 41 different types of demand-driven questions. These 41 questions were organized by the goal to be achieved, and the goals were grouped in the four decision-making stages (i) clarifying a societal problem, its causes and potential impacts; (ii) finding and selecting options to address a problem; (iii) implementing or scaling-up an option; and (iv) monitoring implementation and evaluating impacts.ConclusionThe mutually exclusive and collectively exhaustive list of demand-driven questions will help decision-makers (to ask and prioritize questions), evidence producers (to organize and present their work), and evidence-intermediaries (to connect evidence needs with evidence supply).