Aid Of Information Research Articles

Production benefits on encoding are modulated by language experience: Less experience may help.

Several lines of research have shown that performing movements while learning new information aids later retention of that information, compared to learning by perception alone. For instance, articulated words are more accurately remembered than words that are silently read (the production effect). A candidate mechanism for this movement-enhanced encoding, sensorimotor prediction, assumes that acquired sensorimotor associations enable movements to prime associated percepts and hence improve encoding. Yet it is still unknown how the extent of prior sensorimotor experience influences the benefits of movement on encoding. The current study addressed this question by examining whether the production effect is modified by prior language experience. Does the production effect reduce or persist in a second language (L2) compared to a first language (L1)? Two groups of unbalanced bilinguals, German (L1) - English (L2) bilinguals (Experiment 1) and English (L1) - German (L2) bilinguals (Experiment 2), learned lists of German and English words by reading the words silently or reading the words aloud, and they subsequently performed recognition tests. Both groups showed a pronounced production effect (higher recognition accuracy for spoken compared to silently read words) in the first and second languages. Surprisingly, the production effect was greater in thesecond languages compared to the first languages, across both bilingual groups. We discuss interpretations based on increased phonological encoding, increased effort or attention, or both, when reading aloud in a second language.

Exploring the Impact of Packaging Design on Consumer Purchase Behavior for Northeastern Cereal Agricultural Products

This study endeavors to unravel the intricate relationship between packaging design and consumer behavior in the context of Northeastern cereal agricultural products. Focusing on the vibrant region of Song Yuan, Jilin, China, the study employs a multi-method approach encompassing fieldwork observations, content analysis, and consumer surveys. The investigation unfolds in three key dimensions: evaluating current packaging designs, understanding consumer perspectives, and gauging the acceptance of alternative designs. The findings reveal four distinct models of packaging design, each resonating differently with consumers. Traditional motifs evoke a sense of authenticity, while modern graphics enhance visual appeal and information communication. Consistency with brand identity instills trust, and informative packaging aids in informed purchase decisions. Additionally, the introduction of alternative designs presents an exciting prospect, especially among younger demographics, signifying a potential shift in consumer preferences. This research contributes a nuanced understanding of the pivotal role packaging design plays in shaping consumer choices for Northeastern cereal agricultural products. It provides actionable insights for businesses seeking to align their packaging strategies with consumer preferences, fostering brand loyalty and enhancing market competitiveness. This study serves as a valuable resource for practitioners, academics, and stakeholders invested in the dynamic interplay between design and consumer behavior.

Exploring the Types of Social Support Exchanged by Survivors of Pediatric Stroke and Their Families in an Online Peer Support Community: Qualitative Thematic Analysis.

Pediatric stroke is relatively rare and underresearched, and there is little awareness of its occurrence in wider society. There is a paucity of literature on the effectiveness of interventions to improve rehabilitation and the services available to survivors. Access to online health communities through the internet may be a means of support for patients with pediatric stroke and their families during recovery; however, little research has been done in this area. This study aims to identify the types of social support provided by an online peer support group to survivors of pediatric stroke and their families. This was a qualitative thematic analysis of posts from a pediatric stroke population on a UK online stroke community active between 2004 and 2011. The population was split into 2 groups based on whether stroke survivors were aged ≤18 years or aged >18 years at the time of posting. The posts were read by 2 authors who used the adapted Social Support Behavior Code to analyze the types of social support exchanged. A total of 52 participants who experienced a pediatric stroke were identified, who posted a total of 425 messages to the community. About 41 survivors were aged ≤18 years at the time of posting and were written about by others (31/35 were mothers), while 11 were aged >18 years and were writing about themselves. Survivors and their families joined together in discussion threads. Support was offered and received by all participants, regardless of age. Of all 425 posts, 193 (45.4%) contained at least 1 instance of social support. All 5 types of social support were identified: informational, emotional, network, esteem support, and tangible aid. Informational and emotional support were most commonly exchanged. Emotional support was offered more often than informational support among participants aged ≤18 years at the time of posting; this finding was reversed in the group aged >18 years. Network support and esteem support were less commonly exchanged. Notably, the access subcategory of network support was not exchanged with the community. Tangible aid was the least commonly offered type of support. The exchanged social support provided insight into rehabilitation interventions and the unmet needs of pediatric stroke survivors. We found evidence of engagement of childhood stroke survivors and their families in an online stroke community, with peer support being exchanged between both long- and short-term survivors of pediatric stroke. Engagement of long-term survivors of pediatric stroke through the online community was key, as they were able to offer informational support from lived experience. Further interventional research is needed to assess health and rehabilitation outcomes from engagement with online support groups. Research is also needed to ensure safe, nurturing online communities.

An artificial intelligence language model improves readability of burns first aid information

AimsThis study aimed to assess the potential of using an artificial intelligence (AI) large language model to improve the readability of burns first aid information. MethodsAn AI language model (ChatGPT-3) was used to rewrite content from the top 50 English-language webpages containing burns first aid information to be understandable by an individual with the literacy level of an 11-year-old, as recommended by the American Medical Association and Health Education England. The assessment of readability was conducted using five validated tools. ResultsIn their original form, only 4% of the patient education materials (PEMs) met the target readability level across all tools. The median grade was 6.9 (SD=1.1). One-sample one-tailed t-test revealed that this was not significantly below the target (p = .31). After AI-modification, 18% of PEMs reached the target level using all tools, with a median grade of 6 (SD=0.9), which was significantly below the target level (p < .001). Once rewritten using AI, paired t-test demonstrated that all readability scores improved significantly (p < .001). ConclusionUtilising an AI language model proved an effective and viable method for enhancing readability of burns first aid information.

Basic Disaster Skills During and After Disaster According to the Opinions of Firefig

This qualitative research was conducted with 19 experienced firefighters from the İzmir Metropolitan Municipality Fire Department, aiming to identify the essential skills citizens need during and after disasters. The study includes in-depth insights and experiences of the participants. According to the research results, primary skills highlighted during disasters include remaining calm, communication proficiency, moving to safe zones, trusting and collaborating with experts, and minimizing damage. Secondary skills emphasize practical abilities such as going to assembly points, reaching high places, possessing first aid knowledge, effective communication and information sharing, and preventing hazardous situations. The research indicates that citizens are expected to focus on primary skills like assisting search and rescue teams, going to assembly points, maintaining healthy communication and coordination, staying calm and hopeful, and collaborating with search and rescue teams during and after disasters. Secondary skills such as first aid, hygiene, personal care, social and psychological support aim to protect citizens' health, provide emotional support, and strengthen community solidarity after disasters. This research contributes significantly to promoting active citizen participation in disaster management and the development of curricula and policies based on disaster literacy skills.

Assessing capacitance soil moisture sensor probes’ ability to sense nitrogen, phosphorus, and potassium using volumetric ion content

Accurate and near real-time volumetric soil water and volumetric ion content (VIC) measurements can both inform precise irrigation scheduling and aid in fertilizer management applications in cropping systems. To assist in the monitoring of these measurements, capacitance-based soil moisture probes are used in agricultural best management practice (BMP) programs. However, the ability of these sensors to detect nutrients in the soil sourced from fertilizers is not well understood. The objective of this study was to evaluate the sensitivity of a capacitance-based soil moisture probe in detecting Nitrogen (N), Phosphorous (P), and Potassium (K) movement in the soil. To achieve this, a laboratory-based setup was established using pure sand soil cores. Raw soil moisture and VIC probe readings from the cores were contrasted across multiple N, P, and K rates. The N treatments applied were rates of 0, 112, 168, and 224 kg/ha; for P, were 0, 3.76, and 37.6 kg/ha, and for K were 0, 1.02, 1.53, and 2.04 kg/ha. Each nutrient was evaluated separately using a randomized complete block design experiment with three replications for N and K, and 5 replications for P. The impact of each nutrient rate on the sensitivity of VIC readings was determined by evaluating differences in three points of the time series, including the observed maximum point, inflection point, and convergence value as well as the time of occurrence of those points over a 24-hour period. These points were assessed at depths 5, 15, 25, 35, 45, and 55 cm. The findings of this study highlight the capacitance-based soil moisture probes’ responsiveness to changes in all K rates at most depths. However, its sensitivity to changes in N and P rates is comparatively lower. The results obtained in this study can be used to develop fertilizer management protocols that utilize K movement as the baseline to indirectly assess N and P, while helping to inform those who currently use the probe which nutrients the probe may be detecting. The probes’ readings could be incorporated into decision support systems for irrigation and nutrient management and improve control systems for precision water and nutrient management.

Development of a Cancer Pathway Support Guide for Patients and Carers: A Codesign Project

Background. Cancer diagnosis is a complex and multifaceted process that can be stressful and anxiety-provoking for patients. Evidence-based tools and information aids that can be used for guiding and supporting patients during cancer investigations and after diagnosis are limited. This paper presents a user-centred codesign project that aims to develop a cancer pathway support tool for patients, carers, and healthcare professionals. Method. A mixed-method codesign approach was used including prototype development (January–March 2022), three online codesign workshops (April–June 2022), one-to-one feedback, and beta testing informed by the standardised Theoretical Framework of Acceptability (TFA) questionnaire (July–October 2022). Nine individuals with lived experience of cancer contributed to the project and are referred to as codesigners. Results. The codesigners valued the potential importance of a tool that can be used by the patients and carers if they want specific information about cancer investigations, diagnosis, and treatment. The ability to select what they need as opposed to long leaflets and generating their own questions for the healthcare providers were highlighted as important aspects of improving patient care. The tool was collectively designed to provide clear definitions of the cancer care pathway and easily accessible links from trusted resources and includes practical information to minimise the burden that can be experienced from preparation for appointments and tests. Beta testing results with a small sample of potential users including patients, carers, and healthcare providers (n = 23) showed high acceptability of the guide (range = 7–35, mean = 28.52, and standard deviation = 3.88) based on the TFA questionnaire. Conclusion. A cancer pathway support tool called “Your Cancer Pathway Support Guide (YCPSG)” was developed as a result of an iterative codesign process aiming to improve patient experience and outcomes for people referred on a suspected cancer pathway or who have been diagnosed with cancer. The tool provides information and support in both digital and PDF formats. Further studies are needed to evaluate the potential impact of “Your Cancer Pathway Support Guide” on patient outcomes and experience and the wider system.

Improved Synchronous Sampling and Its Application in High-Speed Railway Bearing Damage Detection

Synchronous analysis is one of the most effective and practical techniques in rotating machinery diagnostics, especially in cases with variable speed operations. A modern analog-to-digital convertor (ADC) usually digitizes an analog signal to an equal time interval data series. Synchronous resampling converts the data series from an equal time interval data series to an equal shaft rotation angle interval data series. This conversion is usually achieved in the digital domain with the aid of shaft speed information, through either direct measurement or identification from a measured vibration signal, which is a time-consuming process. In order to improve the computational efficiency as well as the data processing accuracy, in this paper, a fast synchronous time-point calculation method based on an inverse function interpolation procedure is proposed. By identifying the inverse function of the instantaneous phase with respect to time, the calculation process of synchronous time points is optimized, which results in improved calculation efficiency and accuracy. These advantages are demonstrated by numerical simulations as well as experimental verifications. The numerical simulation results show that the proposed method can improve calculation speed by about five times. The synchronous analysis based on the proposed method was applied to a bearing fault detection in a high-speed rail carriage, which demonstrated the advantages of the proposed algorithm in improving the signal-to-noise ratio (SNR) for bearing damage feature extraction.

CRAFT: a web-integrated cavity prediction tool based on flow transfer algorithm

Numerous computational methods, including evolutionary-based, energy-based, and geometrical-based methods, are utilized to identify cavities inside proteins. Cavity information aids protein function annotation, drug design, poly-pharmacology, and allosteric site investigation. This article introduces “flow transfer algorithm” for rapid and effective identification of diverse protein cavities through multidimensional cavity scan. Initially, it identifies delimiter and susceptible tetrahedra to establish boundary regions and provide seed tetrahedra. Seed tetrahedron faces are precisely scanned using the maximum circle radius to transfer seed flow to neighboring tetrahedra. Seed flow continues until terminated by boundaries or forbidden faces, where a face is forbidden if the estimated maximum circle radius is less or equal to the user-defined maximum circle radius. After a seed scanning, tetrahedra involved in the flow are clustered to locate the cavity. The CRAFT web interface integrates this algorithm for protein cavity identification with enhanced user control. It supports proteins with cofactors, hydrogens, and ligands and provides comprehensive features such as 3D visualization, cavity physicochemical properties, percentage contribution graphs, and highlighted residues for each cavity. CRAFT can be accessed through its web interface at http://pitools.niper.ac.in/CRAFT, complemented by the command version available at https://github.com/PGlab-NIPER/CRAFT/.Scientific contributionFlow transfer algorithm is a novel geometric approach for accurate and reliable prediction of diverse protein cavities. This algorithm employs a distinct concept involving maximum circle radius within the 3D Delaunay triangulation to address diverse van der Waals radii while existing methods overlook atom specific van der Waals radii or rely on complex weighted geometric techniques.

Molecular identification of the causal agent of bacterial wilt in tomato plants in southwestern Paraná

Bacterial wilt, caused by phytopathogenic species of the genus Ralstonia, is one of the main diseases affecting the tomato crop. The Ralstonia solanacearum species complex occurs as a result of variants being widely diverse in terms of adaptation to different climatic conditions, host variations, and aggressiveness, which complicate disease control recommendations. In this study, we employed molecular methods to analyze 26 R. solanacearum isolates collected from tomato plants cultivated in the southwest region of Paraná, Brazil. Isolates were obtained from plants exhibiting wilt symptoms in a protected cultivation system and in an open field. The specific primers 759/760 confirmed the isolates as part of the Ralstonia solanacearum complex, and Nmult primers were used to identify the phylotype. Variability analysis using BOX-PCR with the BOX-A1R primer on 19 isolates revealed molecular diversity. All 26 isolates were confirmed as Ralstonia solanacearum, belonging to phylotype II. Comparison of genomic DNA band patterns amplified by BOX-PCR indicated molecular variability, forming eight groups at a similarity level of 0.63. These results confirm the prevalence of R. solanacearum phylotype II in southwestern Paraná. This information aids decision-making in disease management and contributes to breeding efforts aiming at the development of resistant cultivars.

Evaluating Quality, Credibility, and Readability of Online Over-the-Counter Hearing Aid Information.

With over-the-counter hearing aids being recently approved by the United States Food and Drug Administration, the accuracy and usefulness of online information has not yet been examined. This study evaluates the quality, credibility, readability, and accessibility of online over-the-counter hearing aids education materials. Google was queried using the search term "over-the-counter hearing aids". The top 50 results were categorized into healthcare versus non-healthcare authored resources. The Flesch Reading Ease Score (FRES) and Flesch-Kincaid Grade Level (FKGL) tests were utilized to assess readability, whereas the Currency, Relevance, Authority, Accuracy, and Purpose (CRAAP) test and DISCERN instruments were used to assess quality and credibility. The number of clicks taken to access relevant information on each website was used to assess accessibility. There was no significant difference in FRES or FKGL readability scores between healthcare and non-healthcare authored websites (p = 0.5548, p = 0.5981, respectively), but both readability scores were higher than that of the recommended reading level for patient education materials. There was no significant difference in CRAAP and DISCERN scores between both groups (p = 0.5746, p = 0.1699, respectively). The number of clicks did not significantly differ between healthcare and non-healthcare authored resources (p = 0.4932). This study highlights poor readability and accessibility of virtual healthcare information regarding OTC hearing aids. Although credibility in articles authored by healthcare and non-healthcare professionals was adequate, readability was greatly compromised due to the written information exceeding the recommended United States reading level. Accessibility posed a similar issue, as many sites required multiple clicks to access product information. NA Laryngoscope, 134:3302-3309, 2024.

An AI-based framework for earthquake relief demand forecasting: A case study in Türkiye

Accurately forecasting the demand for relief assistance is crucial for efficient post-disaster relief operations. This paper presents an AI-based framework for earthquake relief demand forecasting, aiming to optimize the distribution of emergency resources. To identify higher-risk areas, the framework integrates historical earthquake data with socioeconomic information. This information aids in identifying regions that require immediate relief assistance. The initial dataset for relief demand quantification includes essential parameters such as Latitude, Longitude, Population, Demand, Supply, Cost, Deaths, Injuries, Infrastructural Damage, and Cascading Disaster. By analyzing these inputs and factors, AI algorithms can quantify the relief demand scenario accurately, resulting in accurate and reliable forecasts of relief supply requirements. The iterative ML techniques in generating comprehensive data for Türkiye greatly enhance the quality and accuracy of relief demand prediction. The prediction models used in the framework, namely Least Angle Regression and Linear Regression, demonstrate exceptional performance in forecasting relief demand. They achieve a remarkable R2 score of 1, indicating a perfect fit to the data, and a Loss Error function value of zero, signifying highly accurate predictions. Additionally, the Decision Tree algorithm showcases strong capabilities in iterative learning, achieving an accuracy rate of 88.99 %. In addition to providing valuable insights for tailored relief strategies, the framework extends beyond relief demand by predicting outcomes such as casualties, injuries, and infrastructure damage. With a comprehensive understanding of the earthquake’s impact, relief organizations can strategize their response and allocate resources accordingly. The thorough methodology and innovative application of ML algorithms within our pioneering AI-based framework significantly contribute to the field. This signifies a fundamental shift in the approach to envisioning and executing relief operations following seismic events.

Penerapan Aplikasi Augmented Reality pada Alat Keselamatan Berkendara Bermotor

Augmented reality is a technology that combines two dimensions into the real world into 3D objects. Augmented reality has been widely used in various sector, especially in the sector of safety. Augmented reality can be used as an information aid. In making this augmented reality, the software needed is Unity, Vuforia SDK, and Figma. The implementation of motorized driving safety in Indonesia is still lacking; therefore, an application is needed that can help motorists choose safe driving using augmented reality. With the application of augmented reality to motorcycle driving safety, it is hoped that it can educate the public about safety when driving to reduce injuries and fatalities.

Feedback Supersaturation Control Crystallization Process of Itaconic Acid

AbstractSeeding metastable zone of itaconic acid in water is determined using a laser dynamic detecting technique, based on which feedback supersaturation control is performed for the crystallization process. The crystallization is strictly confined within the metastable zone throughout the whole process to effectively inhibit the secondary nucleation and the strategy of seed loading is employed to avoid the burst of primary nucleation, resulting in the uniform particle size distribution. With the aid of solution information, continuous feedback control is conducted to keep the supersaturation at a high level accelerating the crystal growth, which significantly shortens the time consumption, and also the effect of seed amount is discussed in this work.

Applications of First-Order Ordinary Differential Equations to Real-Life Systems

This article discussed applications of first-order ordinary differential equations to real-life systems, various types of differential equations with examples are presented. When a dead body is discovered somewhere, Police and detective security agencies are eager to identify the time of death and what caused the death. In this article, Newton’s Law of Cooling is used to estimate the time of death of a dead body discovered at midnight with the aid of information about the body’s surrounding temperature. It was discovered in this study that Newton’s law of cooling only works when the temperature of the body’s surroundings is kept constant. The study also considered other applications of first-order differential equations such as the population growth model and radioactive decay of radioactive isotopes and illustrative examples are given in each case.

Perception of Physicians' Notes Among Parents of Different Health Literacy Levels.

To explore the benefits and challenges of accessing physicians' notes during pediatric hospitalization across parents of different health literacy levels. For this secondary analysis, we used semi-structured interviews conducted with 28 parents on their impressions of having access to their child's care team notes on a bedside table. Three researchers used thematic analysis to develop a codebook, coded interview data, and identified themes. Parent interviews and respective themes were then dichotomized into proficient or limited health literacy groups and compared. Nine themes were identified in this secondary analysis: 6 benefits and 3 challenges. All parents identified more benefits than challenges, including that the notes served as a recap of information and memory aid and increased autonomy, empowerment, and advocacy for their child. Both groups disliked receiving bad news in notes before face-to-face communication. Parents with proficient literacy reported that notes allowed them to check information accuracy, but that notes may not be as beneficial for parents with lower health literacy. Parents with limited literacy uniquely identified limited comprehension of medical terms but indicated that notes facilitated their understanding of their child's condition, increased their appreciation for their health care team, and decreased their anxiety, stress, and worry. Parents with limited health literacy uniquely reported that notes improved their understanding of their child's care and decreased (rather than increased) worry. Reducing medical terminology may be one equitable way to increase note accessibility for parents across the health literacy spectrum.

How gender and political ideology impact women-focused aid to sub-Saharan Africa

ABSTRACT Foreign aid plays a critical role in actualising pro-women outcomes in the Global South. However, there is a paucity of research on how gender and political ideology inform gender-oriented foreign aid to sub-Saharan countries. This study examines how gender and political ideology inform the trends of women-focused foreign aid donations from Australia, Canada, Finland, and Germany to sub-Saharan African nations from 2002 to 2016. During this period, government leaders and ministers in the donor countries were from different genders and political ideologies. The study reveals that female heads of government gave more women-focused aid to sub-Saharan Africa than male heads of government. The study also found that right-wing heads of government allocated more women-focused assistance to sub-Saharan Africa than left-wing politicians. This study points to the importance of the active role of female heads of government in effectively mainstreaming and furthering the idea of gender equality on a global scale.

Embedded EEG Feature Selection for Multi-Dimension Emotion Recognition via Local and Global Label Relevance.

Due to the problem of a small amount of EEG samples and relatively high dimensionality of electroencephalogram (EEG) features, feature selection plays an essential role in EEG-based emotion recognition. However, current EEG-based emotion recognition studies utilize a problem transformation approach to transform multi-dimension emotional labels into single-dimension labels, and then implement commonly used single-label feature selection methods to search feature subsets, which ignores the relations between different emotional dimensions. To tackle the problem, we propose an efficient EEG feature selection method for multi-dimension emotion recognition (EFSMDER) via local and global label relevance. First, to capture the local label correlations, EFSMDER implements orthogonal regression to map the original EEG feature space into a low-dimension space. Then, it employs the global label correlations in the original multi-dimension emotional label space to effectively construct the label information in the low-dimension space. With the aid of local and global relevance information, EFSMDER can conduct representational EEG feature subset selection. Three EEG emotional databases with multi-dimension emotional labels were used for performance comparison between EFSMDER and fourteen state-of-the-art methods, and the EFSMDER method achieves the best multi-dimension classification accuracies of 86.43, 84.80, and 97.86 percent on the DREAMER, DEAP, and HDED datasets, respectively.

Effective image fusion strategies in scientific signal processing disciplines: Application to cancer and carcinoma treatment planning.

Multimodal medical image fusion is a perennially prominent research topic that can obtain informative medical images and aid radiologists in diagnosing and treating disease more effectively. However, the recent state-of-the-art methods extract and fuse features by subjectively defining constraints, which easily distort the exclusive information of source images. To overcome these problems and get a better fusion method, this study proposes a 2D data fusion method that uses salient structure extraction (SSE) and a swift algorithm via normalized convolution to fuse different types of medical images. First, salient structure extraction (SSE) is used to attenuate the effect of noise and irrelevant data in the source images by preserving the significant structures. The salient structure extraction is performed to ensure that the pixels with a higher gradient magnitude impact the choices of their neighbors and further provide a way to restore the sharply altered pixels to their neighbors. In addition, a Swift algorithm is used to overcome the excessive pixel values and modify the contrast of the source images. Furthermore, the method proposes an efficient method for performing edge-preserving filtering using normalized convolution. In the end,the fused image are obtained through linear combination of the processed image and the input images based on the properties of the filters. A quantitative function composed of structural loss and region mutual data loss is designed to produce restrictions for preserving data at feature level and the structural level. Extensive experiments on CT-MRI images demonstrate that the proposed algorithm exhibits superior performance when compared to some of the state-of-the-art methods in terms of providing detailed information, edge contour, and overall contrasts.

Prostate cancer management in Southeast Asian countries: a survey of clinical practice patterns.

Prostate cancer (PC) has a serious public health impact, and its incidence is rising due to the aging population. There is limited evidence and consensus to guide the management of PC in Southeast Asia (SEA). We present real-world data on clinical practice patterns in SEA for advanced PC care. A paper-based survey was used to identify clinical practice patterns and obtain consensus among the panelists. The survey included the demographics of the panelists, the use of clinical guidelines, and clinical practice patterns in the management of advanced PC in SEA. Most panelists (81%) voted prostate-specific antigen (PSA) as the most effective test for early PC diagnosis and risk stratification. Nearly 44% of panelists agreed that prostate-specific membrane antigen positron emission tomography-computed tomography imaging for PC diagnostic and staging information aids local and systemic therapy decisions. The majority of the panel preferred abiraterone acetate (67%) or docetaxel (44%) as first-line therapy for symptomatic mCRPC patients. Abiraterone acetate (50%) is preferred over docetaxel as a first-line treatment in metastatic castration-sensitive prostate cancer patients with high-volume disease. However, the panel did not support the use of abiraterone acetate in non-metastatic castration-resistant prostate cancer (nmCRPC) patients. Apalutamide (75%) is the preferred treatment option for patients with nmCRPC. The cost and availability of modern treatments and technologies are important factors influencing therapeutic decisions. All panelists supported the use of generic versions of approved therapies. The survey results reflect real-world management of advanced PC in a SEA country. These findings could be used to guide local clinical practices and highlight the financial challenges of modern healthcare.