Search Results (49)

Background: Malabsorption is a frequent and clinically relevant condition associated with a high risk of disease-related malnutrition across multiple gastrointestinal and systemic disorders. Despite its prevalence, standardized nutritional management algorithms remain limited. Following a previously published Delphi consensus on the use of oligomeric enteral formulas, the present study aimed to develop and validate a practical nutritional management algorithm for patients with malabsorption. Methods: A structured expert questionnaire was conducted among 173 physicians with clinical experience in malabsorption, including specialists in endocrinology, gastroenterology, internal medicine, and oncology. Participants gained experience using the preliminary algorithm by applying it to five real-life cases before completing the questionnaire. The survey addressed symptom type, intensity, and duration required for screening, diagnostic criteria for malnutrition, timing of reassessment, indications for oligomeric oral nutritional supplements (ONSs), and criteria for reintroducing polymeric formulas. Statistical analyses were performed using SAS v9.4. Results: Of the 173 participants, 67.1% were women, with a mean age of 39.6 ± 8.2 years and a mean clinical experience of 10.9 ± 7.9 years. In clinicians’ opinion, diarrhea was the most frequently considered symptom to trigger screening (88.6%), followed by abdominal distension (72.6%), abdominal pain (65.4%), and increased gastric residuals (45.8%). Experts agreed that symptoms should present with at least moderate intensity and persist for more than 3 weeks to justify screening. Most respondents agreed with using the GLIM criteria for malnutrition assessment (97.7%). For patients with poor tolerance to polymeric ONSs or moderate-to-severe symptoms, initiation of oligomeric ONSs was recommended, with diarrhea identified as the main indication (31.1%). Symptom severity prompting oligomeric formulas was rated between 2.8 and 3.3 on a 5-point scale. The mean recommended duration of symptom improvement before transitioning back to polymeric formulas was 6.24 ± 4.45 weeks. Conclusions: This study presents a consensus-based, clinically applicable algorithm for nutritional screening, diagnosis, and intervention in patients with malabsorption. The algorithm provides clear guidance on symptom assessment, use of GLIM criteria, selection of ONS type, and follow-up, potentially improving standardization and quality of nutritional care in this high-risk population. Full article

Forensic accounting faces increasing complexity as reactive approaches fail to address escalating risks. This study pioneers a Proactive Risk Intelligence Framework (PRIF) for Chartered Accountant (CA) firms, targeting gaps in risk anticipation, stakeholder communication, and compliance. Employing mixed-method-design interviews with 30 risk advisors, case studies, and analysis of 30 forensic reports, the PRIF was developed and validated using thematic coding, risk metrics, and Delphi panel refinement. Integration of AI and blockchain reduced the risk detection time from 47 days post-event to 9–22 days pre-event, with accuracy increasing from 62% to 89–94%. The Stakeholder Communication Index (SCI) revealed a strong correlation (r = 0.83) between report quality and client retention (91% for high SCI vs. 54% for low SCI). PRIF adoption reduced compliance resolution time by 58% and financial misstatements by 47%, yielding an average ROI of 83%. This integrated framework combines real-time monitoring, stakeholder-centric reporting, and dynamic compliance for CA firms. While the findings are based on India-focused samples, practical benefits include scalable toolkits for firms and policy guidance for regulators with a broader impact on financial governance. PRIF shifts forensic accounting from reactive detection to proactive prevention, advancing stakeholder trust and industry standards. Full article

The operation of medical cyclotrons for PET radiopharmaceutical production presents significant radiological and environmental challenges that require systematic risk assessment and evidence-based mitigation strategies. In this study, an integrated framework combining Failure Mode and Effects Analysis (FMEA) with a quantitative Defense Effectiveness Factor (DEF) approach to evaluate and reduce residual risk in a real urban cyclotron facility. High-criticality failure modes (Risk Priority Number $\ge 120$) affecting HVAC systems, stack exhaust, and power supply were identified and validated through a Delphi expert consensus process. These modes were addressed with multi-layered defense-in-depth strategies: redundant systems (occurrence reduction, 60–80% effectiveness), real-time monitoring (detection reduction, 40–50% effectiveness), and design robustness (severity reduction, 70–85% effectiveness). The combined DEF yielded a 96–97% risk reduction. One-way sensitivity analysis confirmed the robustness of these results, with residual annual effective dose to the representative person remaining between 50–88 μSv/year (well below the IAEA 1 mSv/year public dose constraint) even under pessimistic scenarios. Primary exposure pathways were inhalation and cloud gamma from ¹⁸F and ⁴¹Ar during the early-morning production window, while secondary pathways were negligible due to the short half-lives of the radionuclides. These findings demonstrate that the integration of FMEA with DEF-based defense-in-depth and Gaussian plume modeling provides a transparent, robust, and regulatory-compliant framework for managing radioactive atmospheric emissions in urban medical cyclotron facilities. Full article

Background: Care for knee osteoarthritis (KOA) is frequently fragmented, and pathway-level decisions within Physical Medicine and Rehabilitation (PM&R) are influenced by local organizations. The objective of this study was to identify areas of agreement and disagreement among PM&R experts and to translate these into a clinically interpretable, function-oriented care pathway for knee osteoarthritis (KOA) within rehabilitation services. Methods: A two-round Real-Time Delphi study was conducted using the SmartDelphi web platform. A steering committee of five PM&R physicians developed a 37-item questionnaire covering referral/access, functional and outcome assessment, conservative management, escalation/referral thresholds, and follow-up/discharge. Round 1 was online (SERMEF osteoarthritis working group; 46 invited, 40 completed; 87.0%) with responses collected until 30 April 2025. Round 2 was an in-person, facilitated validation round on 30 May 2025 at the SERMEF Congress (A Coruña; 85 invited, 70 completed; 82.4%). Items were rated on a 6-point Likert scale; consensus strength was defined by interquartile range (IQR): strong (0–1) vs. weak (≥2). No patient-level data were collected; participant characteristics were comparable across rounds, suggesting consensus refinement reflected deliberation rather than panel shifts over time. Results: Consensus supported a longitudinal, function-first pathway that was structured into five phases: entry/referral to PM&R; comprehensive functional assessment using a minimum outcomes dataset (pain VAS/NRS, WOMAC function, quality-of-life scale); multimodal conservative rehabilitation combining exercise/physiotherapy, education/self-management support, and indicated oral/topical therapies; reassessment-guided escalation in non-responders, reserving interventional PM&R techniques, multidisciplinary musculoskeletal pain-unit management, or orthopedic evaluation for persistent pain and/or functional limitation; and longitudinal monitoring with defined discharge criteria. Conclusions: SERMEF PM&R experts converged on an implementation-oriented, outcomes-driven KOA itinerary centred on functioning, conservative multimodal care, structured reassessment, and explicit discharge planning. Full article

Background: Spasticity, which occurs with certain neurological conditions, substantially affects quality of life, function, and social participation. Despite widespread botulinum toxin use, variability persists in patient information, access to specialized rehabilitation, and follow-up models. Methods: This two-phase Delphi-Dialogue Patients–Professionals study (DDPP), promoted by SERMEF, integrated perspectives from 77 patients and 141 rehabilitation professionals. Phase 1 used parallel surveys to assess satisfaction, perceived effectiveness of botulinum toxin, communication preferences, and rehabilitation follow-up. Phase 2 applied Real-Time Delphi with 38 experts to generate consensus recommendations to improve spasticity management. Results: Patients and professionals agreed on botulinum toxin benefits, the importance of ongoing rehabilitation, and the value of hybrid (in-person/remote) follow-up. Key gaps concerned access to Physical Medicine and Rehabilitation services, clarity and timing of information, and shared goal setting. Experts translated these misalignments into 10 prioritized recommendations, with highest feasibility for actions standardizing access pathways, optimizing botulinum toxin use, reinforcing structured education, and consolidating hybrid rehabilitation models. Conclusions: The DDPP approach offers a reproducible framework to align stakeholder perspectives by converting quantified divergence into consensus-based innovation priorities. Implementing the recommendations—particularly those strengthening communication, education, and hybrid pathways regarding botulinum toxin treatment—may support more accessible, personalized, patient-centered spasticity care. Full article

Recent technological advancements have made Large Language Models (LLMs) easily accessible through apps such as ChatGPT, Claude.ai, Google Gemini, and HuggingChat, allowing text generation on diverse topics with a simple prompt. Considering this scenario, we propose three machine–machine collaboration models to streamline and accelerate Delphi execution time by leveraging the extensive knowledge of LLMs. We then applied one of these models—the Iconic Minds Delphi—to run Delphi questionnaires focused on the future of work and higher education in Brazil. Therefore, we prompted ChatGPT to assume the role of well-known public figures from various knowledge areas. To validate the effectiveness of this approach, we asked one of the emulated experts to evaluate his responses. Although this individual validation was not sufficient to generalize the approach’s effectiveness, it revealed an 85% agreement rate, suggesting a promising alignment between the emulated persona and the real expert’s opinions. Our work contributes to leveraging Artificial Intelligence (AI) in Futures Research, emphasizing LLMs’ potential as collaborators in shaping future visions while discussing their limitations. In conclusion, our research demonstrates the synergy between Delphi and LLMs, providing a glimpse into a new method for exploring central themes, such as the future of work and higher education. Full article

Background: Chronic Brain Injury (CBI) is a lifelong condition requiring continuous adaptation by patients, families, and healthcare professionals. Transitioning rehabilitation toward patient-centered and self-management approaches is essential, yet remains limited in Spain. Methods: We conducted a two-phase consensus study in collaboration with the Spanish Society of Physical Medicine and Rehabilitation (SERMEF) and the Spanish Federation of Brain Injury (FEDACE). In Phase 1, surveys were distributed to patients (214 invited; 95 complete responses, 44.4%) and physiatrists (256 invited; 106 valid responses, 41.4%) to capture perceptions of current rehabilitation practices, including tele-rehabilitation. Differences and convergences between groups were analyzed using a Synthetic Factor (F). In Phase 2, a panel of 21 experts applied a real-time eDelphi process (SmartDelphi) to assess the feasibility of proposed innovations, rated on a six-point Likert scale. Results: Patients and professionals showed both alignment and divergence in their views. Patients reported lower involvement of rehabilitation teams and expressed more reluctance toward replacing in-person care with telemedicine. However, both groups endorsed hybrid models and emphasized the importance of improved communication tools. Expert consensus prioritized feasible interventions such as online orthopedic renewal services, hybrid care models, and educational video resources, while less feasible options included informal communication platforms (e.g., WhatsApp) and bidirectional teleconsultations. Recommendations were consolidated into five domains: (R1) systemic involvement of rehabilitation teams in chronic care, (R2) patient and caregiver education, (R3) self-management support, (R4) communication tools, and (R5) socialization strategies. Conclusions: This study demonstrates the value of combining patient and professional perspectives through digital Delphi methods to co-design innovation strategies in CBI rehabilitation. Findings highlight the need to strengthen communication, provide structured education, and implement hybrid care models to advance patient-centered rehabilitation. The methodology itself fostered engagement and consensus, underscoring its potential as a tool for participatory healthcare planning. Full article

Commercial complexes, as major sources of urban energy consumption and carbon emissions, face urgent demands for efficiency improvement under the “dual-carbon” strategy. This paper develops a Building Information Modeling (BIM)-enabled life-cycle energy management framework to address fragmented monitoring, weak coordination, and data silos inherent in traditional approaches. Methodologically, a structured literature review was conducted to identify inefficiencies and draw lessons from global practices. An enhanced Delphi method was then applied to refine 12 key evaluation indicators spanning six dimensions—policy, economic, social, technological, environmental, and compliance—which were subsequently integrated into a BIM platform. This integration enables real-time energy monitoring, multi-system diagnostics, and cross-phase collaboration across the design, construction, and operation stages. An empirical case study of the Zhongjian Plaza project in Shanghai demonstrates that the proposed framework not only enhances energy efficiency and reduces life-cycle costs, but also improves user comfort while aligning with both domestic green building standards and international sustainability targets. Overall, the study provides a replicable methodology and practical reference for the smart and low-carbon operation of large-scale commercial complexes, thereby offering strategic insights for advancing sustainable urban development. Full article

Risk identification is a foundational process in construction project management, yet the selection of appropriate identification techniques often lacks empirical guidance. To address this gap, this study adopts a case study design and conducts a comparative evaluation of four established but underutilized methods—Delphi, Nominal Group Technique (NGT), Hazard and Operability Study (HAZOP), and Preliminary Hazard Analysis (PHA)—within the context of a large-scale infrastructure project in Türkiye. The Delphi panel consisted of five senior experts. The NGT session involved six site-level practitioners, and the HAZOP team was composed of four multidisciplinary professionals. Two project-level managers conducted the PHA. Each technique was assessed against seven evaluative criteria: methodological structure, stakeholder engagement, analytical depth, resource intensity, flexibility, decision-support value, and contextual fit. The findings reveal that HAZOP achieved the highest analytical depth and decision-support capacity, while NGT demonstrated the strongest stakeholder engagement and contextual adaptability. Delphi provided robust systemic insights but required substantial time and expert availability, whereas PHA offered rapid screening capacity with limited depth. Drawing on these findings, the study proposes a Contextual Decision Support Model that helps practitioners select the most suitable technique based on project complexity, available resources, and stakeholder conditions. This practical framework enables construction professionals to balance methodological rigor with contextual feasibility, ensuring that risk identification processes are both systematic and adaptable to real-world constraints. Beyond its methodological contribution, the study advances risk management in construction by providing a structured and transparent decision-support approach that bridges academic rigor with on-site practice. By aligning method selection with project-specific attributes and stakeholder dynamics, the model strengthens the integration of analytical precision and practical decision-making across the project lifecycle, thereby contributing to more proactive, evidence-based, and resilient risk management in construction projects. Full article

Proper rehabilitation following rotator cuff repair (RCR) is necessary for successful postoperative outcomes, though the average course of physical therapy (PT) is lengthy and costly. The goals of this study were to (1) develop exercise programs for the CuffLink mHealth system and (2) evaluate early prototype efforts at meeting the needs of RCR clients. A panel of 24 clinicians participated in a Delphi study to identify consensus in rehabilitation, key informatic needs, and appropriate interface modalities for client usage. Utilizing the Delphi findings, the iA CuffLink mHealth system was developed, and a pilot evaluation assessed the feasibility and usability of CuffLink through the mHealth App Usability Questionnaire (MAUQ). During the pilot evaluation, the overall MAUQ score was 6.14. All participants (n = 18) viewed messaging the care team and a real time rep counter as “important” or “very important”. All participants either agreed or strongly agreed that quantifying progress would help motivate them to be compliant, and that the app helped them achieve their recovery outcomes compared to the shoulder device alone. Participants were generally pleased with the ease of use, information arrangement, and usefulness of CuffLink. These findings can advance our understanding of the informatics and usability needs in telerehabilitation systems. Full article

Background/Objectives: Antimicrobial resistance (AMR) is a major global health threat, with children at higher risk due to developmental differences in drug metabolism, limited treatment options and inappropriate antibiotic use. Pediatric antimicrobial stewardship programs (ASPs) face implementation challenges, often relying on adult-based guidelines and limited pediatric-specific evidence. This study aimed to identify and prioritize the most critical areas for pediatric ASP intervention development through a structured, multi-round Delphi consensus process with experts in antimicrobial stewardship and infectious diseases. Method: A four-round modified Delphi process was conducted to identify and prioritize key pediatric ASP interventions. Experts in antimicrobial stewardship and infectious diseases were recruited through an existing clinical trial. Using an iterative survey and in-person discussions, experts provided input on priority areas, which were thematically grouped and refined across rounds. Structured feedback supported real-time refinement and consensus-building. Results: Twenty experts participated in the process, generating 25 priority items in Round 1 through open-ended responses. These were narrowed to seven key priorities through structured voting and discussion. The final items were clustered into three intersecting themes: Care Settings, Prescriptions, and Strategies. Care Settings focused on high-impact areas such as outpatient clinics and intensive care units, where misuse is common and/or care is complex. The prescriptions theme prioritized shorter durations and narrow-spectrum agents. The strategy theme highlighted the need for outcome-based metrics, improved diagnostic stewardship, and routine tracking of patient outcomes to guide and assess stewardship efforts. Conclusions: This expert consensus identified key priorities for pediatric ASPs, providing a foundation for future interventions. Findings can be used to inform policy and practice, improving the appropriate use of antimicrobials in pediatrics and combating AMR. Full article

Vessel arrival priority determination is a critical factor affecting port safety and efficiency in maritime traffic management, yet existing approaches relying on First Come, First Served (FCFS) principles or empirical judgment have limitations in systematic decision-making. This study aims to develop a systematic decision-making framework that overcomes these limitations by creating an automated, expert knowledge-based priority determination system for vessel traffic services. A dynamic score-based vessel arrival priority determination model was developed integrating the Delphi technique and Fuzzy Analytic Hierarchy Process (Fuzzy AHP). Basic score evaluation factors were derived through Delphi surveys conducted with 50 field experts, and weights were calculated by differentially applying Fuzzy AHP and conventional AHP according to hierarchical complexity. The proposed model consists of a dynamic scoring system integrating basic scores reflecting vessel characteristics and operational conditions, special situation scores considering emergency situations, and risk scores quantifying safety intervals between vessels. To validate the model performance, simulation-based evaluation with eight scenarios was conducted targeting experienced VTS (Vessel Traffic Services) officers, demonstrating strong agreement with expert judgment across diverse operational conditions. The developed algorithm processes real-time maritime traffic data to dynamically calculate priorities, providing port managers and maritime authorities with an automated decision support tool that enhances VTS management and coastal traffic operations. Full article

This study explores the integration of Business Intelligence (BI) and Operations Research (OR) as a driver of sustainability within the evolving framework of Logistics 4.0. As logistics systems face pressures from environmental regulations, digital transformation, and stakeholder expectations, the intersection of data analytics and optimization emerges as a critical lever for sustainable operations. Grounded in a Delphi study conducted in a Portuguese logistics firm, this research captures expert consensus across five dimensions of BI implementation: data infrastructure, real-time decision-making, operational transparency, stakeholder coordination, and sustainability performance monitoring. Methodologically, this study employed two iterative Delphi rounds with 61 cross-functional professionals directly engaged with the organization’s BI systems, particularly Microsoft Power BI. Findings indicate that integrating BI with OR models enhances organizational capacity for proactive scenario planning, carbon footprint reduction, and ESG-aligned decision-making. The results also underscore the importance of cross-departmental collaboration, data governance maturity, and user training in fully leveraging BI for sustainable value creation. By providing both theoretical insights and practical guidance, this study advances the emerging discourse on data-driven sustainability in logistics. It offers actionable insights for logistics managers, sustainability strategists, and policymakers seeking to operationalize digital sustainability and embed intelligence-driven approaches into resilient, low-carbon supply chains. Full article

Solvents represent the quiet majority in biomolecular systems, yet modeling their influence with both speed and ri:gor remains a central challenge. This study maps the state of the art in implicit solvent theory and practice, spanning classical continuum electrostatics (PB/GB; DelPhi, APBS), modern nonpolar and cavity/dispersion treatments, and quantum–continuum models (PCM, COSMO/COSMO-RS, SMx/SMD). We highlight where these methods excel and where they falter, namely, around ion specificity, heterogeneous interfaces, entropic effects, and parameter sensitivity. We then spotlight two fast-moving frontiers that raise both accuracy and throughput: machine learning-augmented approaches that serve as PB-accurate surrogates, learn solvent-averaged potentials for MD, or supply residual corrections to GB/PB baselines, and quantum-centric workflows that couple continuum solvation methods, such as IEF-PCM, to sampling on real quantum hardware, pointing toward realistic solution-phase electronic structures at emerging scales. Applications across protein–ligand binding, nucleic acids, and intrinsically disordered proteins illustrate how implicit models enable rapid hypothesis testing, large design sweeps, and long-time sampling. Our perspective argues for hybridization as a best practice, meaning continuum cores refined by improved physics, such as multipolar water, ML correctors with uncertainty quantification and active learning, and quantum–continuum modules for chemically demanding steps. Full article

This study presents the partial real-world validation of a previously developed framework that integrates Lean Construction principles, Building Information Modeling (BIM), and Emerging Technologies to optimize construction management. While the original framework was validated through expert consensus using the Delphi Method, this research applies it in the context of Saudi Arabia to test its feasibility during the design phase. A case-based approach was adopted involving a confidential mega-scale project. Key Performance Indicators (KPIs) were used to assess impact, including cost and time efficiency, productivity, waste reduction, quality, safety, stakeholder satisfaction, and process automation. Our results revealed a 25% improvement in cost efficiency, a 40% acceleration in design delivery, a 25% increase in productivity, 70% process optimization and automation, 100% elimination of non-value-adding activities, and a 20% enhancement in design quality. Stakeholders reported high levels of satisfaction, citing transparency, real-time collaboration, and enhanced decision-making as major benefits. These findings confirm the framework’s potential for transforming project delivery through integrated digital and Lean strategies. Full article