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Cultivating talent with digital literacy and the capacity for sustainable innovation performance is a critical mission of higher education. Merely enhancing digital skills does not necessarily lead to innovation, and the mechanisms underlying this transformation remain to be clarified. This study adopts human capital theory as its analytical framework and constructs a parallel mediation model, focusing on the dual mediation pathways of proactive social behavior and critical thinking dispositions as “soft skill” mediators. Using a sample of 18,534 undergraduate students from Chinese research universities, the study employed a questionnaire survey and analyzed the data using structural equation modeling and the Bootstrap method to examine the relationship between digital literacy and sustainable innovation performance, as well as the roles of the two mediation pathways. The results indicate a significant positive correlation between Chinese university students’ digital literacy and their sustainable innovation performance. Proactive social behavior and critical thinking dispositions exhibit significant mediating effects between the two variables, forming a dual-path mechanism in which the direct path effect is stronger. The findings deepen our understanding of how digital literacy relates to sustainable innovation performance and provide a reference for higher education institutions in cultivating talent for sustainable innovation. Full article

Background/Objectives: Childhood overweight and obesity represent a major global public health challenge, with increasing prevalence and significant long-term metabolic, cardiovascular, and psychosocial consequences. Standard pediatric weight-management strategies based on lifestyle modification often achieve modest and variable results, highlighting the need for more personalized and scalable approaches. Artificial intelligence (AI) has emerged as a promising tool to enhance prevention, early risk stratification, and management of pediatric overweight and obesity. Methods: This narrative review was conducted through a structured search of PubMed, Scopus, and Web of Science for English-language studies published up to January 2026. The main search terms included “artificial intelligence”, “machine learning”, and “deep learning”, combined with “child”, “adolescent”, “pediatric”, “childhood obesity”, “pediatric overweight”, “body mass index”, “weight management”, “nutrition”, “diet”, “physical activity”, “lifestyle”, and “behavior change”. After title/abstract and full-text screening according to predefined eligibility criteria, the included studies were qualitatively synthesized and grouped by main application domains. The initial database search identified 412 records. After removal of 96 duplicates, 316 records were screened by title and abstract. Full-text assessment was subsequently performed for 175 potentially eligible articles. Following this evaluation, 51 studies met the eligibility criteria and were retained from the database search. Additional relevant articles were identified through manual screening of reference lists and related reviews, resulting in the final set of studies included in the narrative synthesis. Results: The review identified five main domains of AI application in pediatric weight management: risk assessment and prediction, dietary assessment and nutritional support, physical activity and lifestyle monitoring, behavioral and psychological support, and clinical decision support. Across the included literature, AI-based approaches were most frequently applied to predictive modeling using longitudinal BMI or growth trajectories, birth characteristics, parental BMI, sleep duration, physical activity, sedentary behavior, and family or socioeconomic factors. However, the evidence base was largely composed of observational and predictive-modeling studies, whereas interventional studies, real-world implementation studies, and long-term pediatric weight-outcome data remained limited. Conclusions: This narrative review indicates that AI has potential as a complementary tool within multidisciplinary, family-centered pediatric weight-management pathways, particularly for early risk stratification, personalized monitoring, and behavioral support. However, the findings also highlight that current evidence remains mainly exploratory and predictive rather than interventional. Further longitudinal, real-world, and ethically grounded research is required to confirm effectiveness, safety, clinical usefulness, and equitable implementation in pediatric populations. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly transforming cardiology through advanced analytical tools capable of identifying complex patterns across cardiovascular imaging, electrophysiology, and clinical datasets. Machine learning (ML) and deep learning (DL) algorithms are being integrated into echocardiography, cardiac computed tomography (CT), cardiac magnetic resonance imaging (MRI), and electrocardiography (ECG), enabling earlier diagnosis and more personalized cardiovascular care. This narrative review summarizes current clinical and organizational applications of AI in cardiology and discusses emerging concepts related to explainable and trustworthy AI. Methods: A narrative review was conducted according to SANRA recommendations using the PubMed, MEDLINE, Web of Science, and Scopus databases, including peer-reviewed publications from 2015 to 2026 addressing clinical, organizational, and ethical applications of AI in cardiology, with particular emphasis on cardiovascular imaging, electrocardiography, heart failure, digital health, and explainable AI frameworks. Results: Substantial evidence demonstrates that AI-based tools can achieve expert-level performance in cardiovascular imaging interpretation, automated electrocardiographic analysis, and clinical risk prediction. Across multiple cardiovascular settings, AI has been associated with improved diagnostic accuracy, enhanced workflow efficiency, and earlier detection of cardiovascular disease. Predictive models support risk stratification in heart failure and ischemic heart disease, while chatbots and digital health platforms may facilitate patient engagement, remote monitoring, and continuity of care. Despite these advances, important challenges remain, including algorithmic bias, limited transparency, insufficient external validation, data heterogeneity, and barriers to routine clinical implementation. Emerging explainable AI approaches may improve model interpretability, clinician confidence, and the safe adoption of AI-driven decision support systems. Conclusions: Artificial intelligence is rapidly evolving from a research-oriented technology into a clinically relevant component of cardiovascular care. Current evidence indicates that AI can enhance diagnostic performance, improve risk prediction, streamline clinical workflows, and facilitate more personalized management across multiple cardiovascular domains. However, the successful translation of AI into routine practice will depend on robust external validation, transparent decision-making mechanisms, regulatory oversight, and clinician acceptance. The development of explainable and trustworthy AI frameworks represents a critical step toward the safe, ethical, and sustainable integration of AI into modern cardiology. Full article

Wind turbines are increasingly deployed at larger scales and in harsher operating environments, leading to greater structural complexity, stronger load variability, and higher maintenance demands across both drivetrain and structural components. Reported field data indicate that gearboxes and bearings account for approximately 30–40% of total turbine downtime, while blade-related failures contribute roughly 20–25% of reported failure events, primarily through fatigue, delamination, leading-edge erosion, and lightning-induced defects. In parallel, large-scale and offshore turbines show increasing susceptibility to tower fatigue cracking, corrosion-assisted degradation, and flange joint bolt-preload loss under cyclic and environmental loading. This review provides a comprehensive applied-engineering synthesis of failure mechanisms, reliability challenges, and monitoring strategies for major wind turbine components, including gearboxes, bearings, blades, towers, and flange joints. A wide range of condition monitoring, structural health monitoring (SHM), and prognostics and health management (PHM) approaches is critically examined, including vibration analysis, acoustic emission, ultrasonic inspection, infrared thermography, impedance-based sensing, electromagnetic methods, machine vision, SCADA-based diagnostics, and artificial-intelligence-assisted fault classification. The review compares these techniques in terms of detectable damage types, spatial coverage, sensitivity, deployment practicality, and limitations under real operating conditions. In addition, statistical reliability methods and data-driven models are discussed to interpret failure trends and uncertainty. Recent AI-based studies have reported fault classification accuracies exceeding 90% under controlled or semi-controlled conditions; however, their field reliability remains constrained by data imbalance, domain shift, limited labeled failure datasets, model interpretability, and insufficient validation under realistic turbine operating environments. The main contribution of this review is an integrated applied synthesis that connects drivetrain and structural failure mechanisms with measurable monitoring indicators, diagnostic technologies, AI-enabled PHM limitations, and predictive-maintenance decision needs. The paper provides practical guidance for monitoring design, early fault detection, predictive maintenance, and long-term reliability improvement in next-generation wind turbine systems. Full article

The factor structure of the Utrecht Work Engagement Scale (UWES) has been debated, with studies alternately supporting unidimensional and three-factor solutions. This inconsistency may reflect a methodological limitation: conventional confirmatory factor analysis (CFA) cannot always separate general from dimension-specific variance, producing similar fit indices across competing models when a dominant general factor is present. We examined the dimensionality of the UWES-17 and UWES-9 in a sample of 755 Chilean university students, comparing unidimensional, three-factor, second-order, and bifactor models using weighted least squares mean and variance adjusted (WLSMV) estimation appropriate for ordinal data. Bifactor indices, explained common variance (ECV), percent of uncontaminated correlations (PUC), and hierarchical omega (ω_h), were computed to evaluate essential unidimensionality. Results indicated that a general engagement factor explained approximately 85% of common item variance in both versions (ECV ≈ 0.85; ω_h > 0.90), while specific factors for vigor, dedication, and absorption retained negligible reliable variance, particularly absorption (ω_h ≈ 0.00). Measurement invariance by sex was supported for the UWES-9 at the metric level, whereas classical UWES-17 solutions showed instability, including factor collapse and non-convergence of the second-order model. Taken together, findings suggest that the apparent multidimensionality of the UWES may be, at least partly, an artifact of conventional CFA modeling rather than a substantive property of the construct in this student sample. For applied monitoring of student well-being, the UWES-9 total score appears to be the most pragmatic and psychometrically defensible approach for assessing general academic engagement in this Chilean university sample, while institutional well-being monitoring would ideally be further supported by criterion-related, predictive, and sensitivity-to-change evidence. Full article

Renal cell carcinoma (RCC) is a biologically heterogeneous malignancy characterized by variable clinical behavior and diverse molecular phenotypes. Although immune checkpoint inhibitors and targeted therapies have transformed the treatment landscape of advanced RCC, clinically validated biomarkers capable of improving risk stratification, therapeutic-decision making and disease monitoring remain lacking. Kidney injury molecule-1 (KIM-1), also known as hepatitis A virus cellular receptor-1 (HAVCR1) or T-cell immunoglobulin and mucin domain-containing protein-1 (TIM-1), has emerged as a biologically compelling investigational biomarker e because of its close relationship to proximal tubular epithelial injury and renal carcinogenesis. KIM-1 is a transmembrane glycoprotein minimally expressed in normal kidney tissue but markedly upregulated in dedifferentiated proximal tubular epithelial cells following injury, and in clear cell RCC, where its extracellular domain can be shed into plasma and urine. Beyond its role as a marker of tubular injury, KIM-1 participates in immune regulation, phagocytosis, inflammatory signaling and tissue remodeling, supporting its potential relevance to tumor biology. Clinical studies have demonstrated associations between elevated circulating KIM-1 levels and RCC diagnosis, recurrence risk, and survival outcomes, particularly in localized and postoperative disease settings. KIM-1 has additionally been investigated as a therapeutic target through antibody–drug conjugate approaches. Despite promising translational data, important limitations yet remain. Current evidence is predominantly prognostic rather than predictive, and substantial analytical and biological challenges continue to limit implementation. Assay standardization, clinically meaningful cutoffs, specimen selection, timing of sampling, and confounding by chronic kidney disease or nonmalignant renal injury remain incompletely resolved. Furthermore, evidence supporting incremental value beyond established clinicopathologic models remains limited. This review critically evaluates the biological rationale, analytical considerations and clinical evidence supporting KIM-1 in RCC. Particular emphasis is placed on distinguishing prognostic, predictive, pharmacodynamic, and therapeutic applications, as well as defining the evidentiary gaps that must be addressed before clinical implementation. Current evidence is derived predominantly from retrospective and exploratory analyses, and important limitations remain regarding assay standardization, biological specificity, chronic kidney disease-related confounding, and prospective validation. The review concludes with a summary of the evolving landscape of KIM-1-directed biomarker strategies in RCC, which may ultimately contribute to improved biologic risk stratification and biomarker-driven clinical investigation in RCC. Full article

As transportation-related environmental pressures intensify, understanding the psychological mechanisms underlying battery electric vehicle (BEV) adoption has become increasingly important. Drawing on the Value–Attitude–Behavior (VAB) framework, this study investigates how perceived green value, hedonic value, and utilitarian value shape Chinese consumers’ attitudes and purchase intentions toward BEVs, while examining the moderating roles of technophilia, range anxiety, and battery cost. A cross-sectional online survey was conducted in China, yielding 596 valid responses. Partial Least Squares Structural Equation Modeling (PLS-SEM) and Necessary Condition Analysis (NCA) were employed for data analysis. The results show that perceived hedonic value exerts the strongest positive effect on Attitude Toward Using BEVs (β = 0.591, p < 0.001), followed by perceived utilitarian value (β = 0.135, p < 0.001) and perceived green value (β = 0.074, p = 0.026). Attitude Toward Using significantly predicts BEV purchase intention (β = 0.151, p = 0.002). Technophilia significantly moderates the relationship between attitude and purchase intention (β = −0.096, p = 0.002), whereas the moderating effects of range anxiety and battery cost are not significant. The structural model explains 40.9% of the variance in attitude and 24.2% of the variance in purchase intention. NCA results further reveal that hedonic value constitutes the most critical necessary condition for forming favorable attitudes toward BEVs (d = 0.079, p < 0.001). This study contributes to the sustainable mobility literature by extending the VAB framework through the integration of multidimensional perceived value and necessary condition logic within the Chinese BEV context. The findings highlight that experiential and technological enjoyment, rather than environmental concern alone, has become a central driver of BEV adoption in emerging electric mobility markets. Full article

With the global rise in sustainable investment concepts, environmental, social, and governance (ESG) factors have increasingly become important criteria influencing investment decisions. Although institutional investors are paying greater attention to corporate ESG performance, limited evidence exists regarding its impact within the Chinese A-share market. Using panel data from Chinese listed firms during the period 2010–2023, this study employs fixed-effects models with clustered standard errors as the baseline estimation method. To improve the robustness of the findings, Tobit regression, Logit regression, lagged-variable models, heterogeneity analysis, and Hausman tests are further conducted. The empirical findings indicate that the overall ESG score and the individual environmental (E), social (S), and governance (G) dimensions do not exhibit statistically significant effects on institutional ownership in the baseline fixed-effects regressions. The results suggest that ESG performance has not yet become a dominant determinant of institutional investment decisions in China’s capital market. However, the robustness tests based on Tobit and Logit models provide limited evidence that ESG performance may still influence institutional investor behavior under alternative empirical specifications. Furthermore, the heterogeneity analysis reveals that the relationship between ESG dimensions and institutional ownership differs across environmentally related and non-environmentally related firms, although the effects are generally weak and statistically limited. The study contributes to the ESG and institutional investment literature in three important ways. First, it provides updated evidence from the Chinese A-share market over the 2010–2023 period, reflecting the evolving stage of ESG development in emerging economies. Second, it comparatively examines the differentiated roles of environmental, social, and governance dimensions rather than relying solely on aggregated ESG indicators. Third, it highlights the limited and transitional nature of ESG integration among institutional investors in China, where traditional financial indicators continue to play a more important role in investment decisions. The findings provide important implications for policymakers, listed firms, and institutional investors seeking to promote sustainable finance development and improve the effectiveness of ESG disclosure practices in emerging markets. Full article

The results of a multiscale study of fault and fracture geometry, distribution, density, and intensity are reported for Mesozoic platform carbonates cropping out along the axial zones of the southern Apennines fold-and-thrust belt, Italy. By integrating field structural observations with digital outcrop analysis, the study focuses on Cretaceous limestone rocks exposed along natural creeks and artificial trails of the Castelsaraceno area, Raparo Mt., southern Italy. There, the limestone beds are bounded by mm- to cm-thick marly–clayey interbeds, forming a well-layered succession made up of a few m-thick bed packages bounded by several cm-thick clayish interlayers. The carbonate multilayer was first affected by thrust tectonics, with the formation of low-angle intra-carbonate thrust faults and fault bend-folding. Then, the multilayer was crosscut by extensional–transtensional high-angle faults, which displaced the previously formed contractional structural elements, and allowed carbonate exhumation from shallow crustal depths. At outcrop scales, thrust-related deformation was solved by low-angle joints and veins, rare high-angle stylolites, and low-angle sheared fractures displaying reverse kinematics. Quantitative analyses of fracture density (P20) and intensity (P21) conducted on selected portions of the thrust fault zones indicate that the low-angle joints and veins attain their highest values in the vicinity of the main slip surfaces, whereas they are almost absent in the surrounding carbonate host rocks. Plio-Quaternary transtensional deformation was solved by NW–SE- and NE–SW striking faults. The latter fault set, nicely exposed along the flanks of the Raganello Creek, was characterized by right-lateral components of slip. Incipient faults, with ca. 1 cm throw, are made up of vertically discontinuous slip surfaces, which crosscut single bed packages and abut against clayish interlayers. The slip surfaces form conjugate geometries, and are associated to high-angle fractures and veins striking NE–SW, dissecting the bed packages. The fault core is virtually absent, whereas the damage zones are very discontinuous along dip. The P20 values computed for the high-angle fractures and veins increase toward the slip surfaces, whereas the P21 values remain nearly constant. These data are interpreted as being due to fault nucleation processes associated with fracture nucleation within the limestone rocks. NE–SW striking small faults displaying throws between 10 and 60 cm are comprised of through-going main slip surfaces crosscutting multiple bed packages, and poorly developed, discontinuous fault cores flanked by m-thick damage zones. The damage zones include sub-parallel high-angle shear fractures, fractures and veins showing a positive correlation between P20 and P21, whose values increase in the vicinity of the main slip surfaces. Such a positive correlation is interpreted as due to fault growth by linkage and coalescence of pre-existing high-angle fractures, and formation of fault-related joints and veins at the extensional quadrants of single shear fractures. Similarly, large-scale NE–SW striking mature faults with throws on the order of tens of meters, made up of a m-thick fault core and 10 s of m-thick damage zones including sub-parallel fractures and veins, also show a positive P20 and P21 correlation. The main outputs of this work are synthesized into a conceptual model illustrating the transition from thrust-related deformation to extensional–transtensional faulting, documenting the evolution of fracture networks from incipient-to-small-to-mature faults. Full article

This study presents a hybrid artificial intelligence system for latent cardiovascular risk stratification based on publicly available clinical and laboratory data. The proposed system integrates data preprocessing, auxiliary target modeling, latent phenotyping using UMAP and Gaussian mixture models, fuzzy logic-based risk integration, and multilevel predictive modeling. The key contribution of the system is the construction of a proxy target reflecting latent risk progression by combining phenotypic structure, probabilistic indicators, and mortality-related anchor points. Experimental evaluation was conducted on the NHANES dataset. The final analytical cohort included 78,822 adult participants, and the modeling set was divided into training, validation, and test subgroups using a stratified 70/15/15 design. The proposed PhaseFuzzy Hybrid model achieved an accuracy of 0.8390, a balanced accuracy of 0.7302, an F1-score of 0.5225, an MCC of 0.4203, an ROC-AUC of 0.8489, a PR-AUC of 0.5014, and a best LogLoss value of 0.4290 on the test set. The latent phenotyping step also demonstrated acceptable internal validity with a silhouette coefficient of 0.4138 and a confidence of 0.8800. The results demonstrate that the proposed framework identifies hidden cardiometabolic risk factors and provides an interpretable, scalable, and calibration-aware framework for latent cardiometabolic risk stratification and population-level screening. Full article

This narrative review examines how big data analytics supports geospatial decision-making in smart cities through the combined roles of spatial data foundations, GeoAI methods, and urban digital twins. Methodologically, the article follows a structured narrative and critical review design rather than a PRISMA-based systematic review, bibliometric analysis, or meta-analysis. The paper responds to fragmentation across GIScience, smart-city studies, urban analytics, geospatial data engineering, and digital twin research, where related contributions often remain technically rich but weakly integrated from a decision-oriented perspective. Rather than treating geospatial decision-making as an extension of GIS or as a general expression of data-driven governance, the review frames it as a layered socio-technical process through which heterogeneous urban data are transformed into decision-relevant knowledge. The analysis first clarifies the conceptual evolution from GIS to spatial decision support and urban governance, and then examines the spatial data sources, integration problems, and representational limits that shape smart-city evidence. It also reviews GeoAI and geospatial analytics methods, including spatial statistics, machine learning, spatiotemporal forecasting, graph-based modeling, optimization, and explainable GeoAI. Urban digital twins are then analyzed as decision infrastructures that connect sensing, data integration, synchronization, semantic modeling, simulation, visualization, user interaction, and feedback into planning or operations. The review further maps these capabilities across mobility, land use, utilities, risk management, environmental resilience, public health, and cross-domain decision contexts. Overall, the paper argues that the value of smart-city geoinformation systems depends not on data abundance or model sophistication alone, but on their capacity to support interpretable, accountable, and context-sensitive urban decisions. Full article

This work aims to determine whether pre-seismic ground dislocations extracted from interferometric satellite synthetic aperture radar (InSAR) imagery contain predictive information for distinguishing between two magnitude classes of an upcoming earthquake, assuming that an earthquake’s occurrence is already imminent. For this reason, twenty-three earthquakes of various magnitudes that occurred in Greece during the year 2020 were analyzed using SAR data to construct a time-series of five six-day InSAR images for each earthquake, spanning a total 24-day period before the earthquake. For each earthquake, four ground dislocation images covering the area around each earthquake were derived from the interferograms, each showing the dislocation during a six-day time interval. Images showing the total ground dislocation during the entire 24-day period before the earthquake were also produced by fusing the four images. Three machine learning classifiers were used to relate the earthquake magnitude class to pre-seismic ground dislocations. High accuracies were obtained with both support vector machine (SVM) and random forest (RF), yet they were highly dependent on the type of images used. In a subsequent analysis, five regression models were applied to estimate the earthquakes’ epicenters from dislocation images. The results reveal that the proposed approach is able to achieve well-localized epicentral area prediction, indicating the potential predictive value of this tool for seismic hazard assessment and emergency planning. Full article

Platform algorithms are widely used to personalize content and organize users’ everyday social media experiences. Yet they may also become objects of resistance when algorithmic recommendations are perceived as intrusive, repetitive, or difficult to escape. Drawing on the critical theory of technology, this study develops a parallel mediation model to explain why users resist algorithm-driven social media platforms. Focusing on algorithmic power and algorithmic technicality as two perceived characteristics of platform algorithms, the model examines whether these perceptions are associated with algorithmic resistance through fairness evaluations and negative emotions. Based on survey data from users of Chinese algorithm-driven social media platforms, the results show that both algorithmic power and algorithmic technicality are associated with stronger algorithmic resistance through lower fairness evaluations and stronger negative emotions. These findings suggest that algorithmic resistance is not merely a response to inaccurate or opaque recommendations, but also reflects users’ reactions to algorithms experienced as systems of platform control and data-driven inference. By identifying fairness evaluations and negative emotions as parallel cognitive and affective pathways, this study shifts attention from algorithmic acceptance to algorithmic resistance and provides a more critical understanding of user agency in human–algorithm relations. Full article

Background/Objectives: This preliminary, single-centre study evaluated infectious diseases consultation (IDC) patterns as indicators of hospital workflow and care complexity, aiming to characterise routinely available variables that may inform future organisational research and EHR-based clinical decision support development. Methods: In this retrospective study, 39,275 IDC requests from 16,430 patients were analysed using hospital information management system records. Paediatric patients and specialised immunosuppressed patient units were excluded. Request volumes, diagnostic categories, consultation purposes, and factors associated with in-hospital mortality were evaluated. Multivariable logistic regression models were constructed separately for two hospital blocks. Results: A total of 39,275 IDC records for 16,430 unique patients were reviewed. Mean consultation access time was 82.2 ± 64.3 min. Requests originated from surgical clinics (43.8%), followed by intensive care units (37.6%) and medical/internal clinics (18.6%). Pneumonia was the most common indication (30.5%), followed by unspecified infections (25.4%) and skin/soft tissue infections (17.2%). Consultation objectives included treatment, diagnostic assessment, and clinical guidance as non-mutually exclusive components. Significant block-level differences were observed in consultation timing, ICU-related consultation, diagnostic profiles, consultation purposes, and mortality. Age and ICU-related consultation were independently associated with mortality in both blocks, whereas consultation access time and COVID-19 diagnosis showed block-specific associations. Conclusions: IDC patterns may reflect not only diagnostic demand but also case severity, ICU-related care, consultation timing, and hospital location. As a preliminary single-centre study, these hypothesis-generating findings highlight the importance of integrating clinical, organisational, and contextual variables in future prospective, multi-centre studies aimed at developing EHR-based decision-support models. External validation, incorporation of comorbidity indices and microbiological data, and assessment of explainability are required before clinical implementation. Full article

Background/Objectives: Treatment for laryngeal cancer often impacts voice, swallowing, communication, and quality of life. Patient-reported outcome measures (PROMs) are increasingly used to evaluate survivorship, but their application and connection with objective functional measures vary widely. The objective was to explore how PROMs are used in laryngeal cancer research, identify the functional areas they assess, analyze their link with objective clinical outcomes, and identify methodological gaps in current studies. Methods: This scoping review followed PRISMA-ScR guidelines. Searches were conducted in PubMed/MEDLINE, Scopus, and Web of Science from their start until April 2026. Included studies involved adults with laryngeal cancer reporting PROMs and/or objective functional outcomes. Data on study features, PROM tools, evaluated domains, and how PROMs relate to objective outcomes were extracted and summarized descriptively. Results: Ninety-five studies with 10,807 participants were included. Most were observational (84.2%) and conducted at a single center (72.6%). Voice-related outcomes were the most common (86.3%), followed by psychological (72.6%) and swallowing outcomes (65.3%). Less frequently assessed were nutritional (22.1%) and supportive care domains (41.1%). The Voice Handicap Index family was the most used PROM group (30.5%). Over half the studies reported PROMs and objective measures separately without statistical integration (51.6%), while only 13.7% performed analytical integration, and none used predictive multivariable models. Significant variation existed in PROM choices, assessed domains, and integration approaches. Conclusions: PROM use in laryngeal cancer survivorship research is heterogeneous and predominantly focused on voice-related outcomes. Limited analytical integration with objective measures hampers a comprehensive understanding of recovery. There is a need for standardized, multidimensional assessment frameworks that include functional, nutritional, psychosocial, and objective outcomes to effectively support patient-centered survivorship care and rehabilitation planning. Full article