Abstract: Background: Knowledge management in global health is essential in response to ageing populations, increasing morbidity, and rising expectations of care. The Knowledge Model about Person Care promotes health systems organized around individuals rather than diseases. Within this framework, vulnerability—understood as the risk of physical or moral harm—can be assessed through Basic Care Variables (BVC) that determine individuals’ need and capacity for self-care. Primary care health information systems provide an opportunity to operationalize these variables at the population level. Methods: This study applies Deductive Methodology to extrapolate community-level health indicator data to population-level vulnerability measures. Using the electronic Primary Care Objective Monitoring tool (e-SOAP) from the Community of Madrid, we analyzed health and social care indicators derived from primary care clinical information systems. The mathematical architecture of selected indicators was used as an approximation to Model-Based Systems Engineering. Results: Primary care indicators enabled the identification and aggregation of community-level data reflecting BCV. The system supports multi-level analysis (regional, managerial, institutional, and professional), facilitating grouped and anonymized data extraction for future vulnerability assessment. Conclusion: A minimum set of primary care indicators can effectively estimate community vulnerability, supporting person-centred health system management and informed decision-making.

Abstract: We evaluated global radiotherapy practices in the management of early-stage (AJCC/UICC 8th edition stages I-II) glottic cancer (ESGC). A cross-sectional online survey was conducted in March 2025 across centers worldwide. Data was collected on clinical practices, including staging, CT simulation, target volumes delineation, organs-at-risk contouring, radiotherapy techniques, dose and fractionation schedules, treatment delivery techniques, and image guidance practices. A total of 181 responses were received, primarily from Asia (41.4%) and Europe (24.3%). Most respondents were from non-academic public centers (44.2%), with multidisciplinary team involvement reported by 84.5%. Head and neck CT scan was the most used staging modality (80.1%). Intensity-Modulated Radiation Therapy was the most common planning technique (82.9%). Hypofractionated radiotherapy schedules predominated for T1 (84%) and T2 (72.4%) disease. T1a was typically treated with whole-larynx target volume (72.4%). Use of ipsilateral involved vocal cord irradiation varied by geographical region (p = 0.015), being most common in North America (44.8%) and Europe (38.6%). Accelerated fractionation for T2 also differed significantly (p < 0.001), with the highest use reported in North America (41.4%). Daily Cone-Beam Computed Tomography was acquired by (58.2%). 70% of respondents expressed interest in the results of a future phase III randomized trial comparing stereotactic body radiation therapy to conventional radiotherapy. Significant global variations in radiotherapy practices for ESGC were observed, likely reflecting disparities in access and differences in institutional protocols. The development and implementation of standardized, evidence-based global guidelines are essential to harmonize care, minimize toxicity, and improve outcomes for patients with ESGC.

Abstract: The Johnson–Mehl–Avrami–Kolmogorov (JMAK) formalism provides a classical framework for describing polymer crystallization kinetics; its applicability under finite-domain confinement requires quantitative assessment. In this work, the influence of one-dimensional geometric restriction on cylindrical growth in polymer thin films is investigated using a stochastic Monte Carlo approach. The model considers site-saturated nucleation on randomly distributed cylindrical nanofibers with constant radial growth velocity under hard-wall boundary conditions. Crystallization kinetics were evaluated through automated segmented regression of the double-logarithmic JMAK representation. Under confinement, the Avrami plot departs from single-slope linearity and exhibits two successive quasi-linear regimes characterized by effective parameter pairs (n₁, ln k₁) and (n₂, ln k₂). The primary exponent n₁ remains thickness-independent, consistent with early-stage radial expansion prior to boundary interaction. The secondary exponent n₂ displays a non-monotonic dependence on reduced film thickness, reflecting the competing influence of wall-induced truncation and inter-fiber impingement on late-stage transformation. These results support a geometric interpretation in which finite-domain constraints modify effective growth dimensionality and provide a reproducible framework for analyzing dual-regime Avrami behavior in confined crystallization systems.

Abstract: Early cancer detection has historically relied on episodic, population-based screening strategies interpreted against fixed thresholds. Although effective in selected contexts, such approaches detect disease primarily after structural or biochemical abnormalities become overt. Advances in genomics, liquid biopsy, and metabolomics now permit a conceptual transition from static screening toward longitudinal, biologically calibrated surveillance. This review proposes an integrated early-detection architecture grounded in four complementary dimensions of tumorigenesis: inherited susceptibility, somatic field evolution, molecular residual disease, and functional metabolic remodeling. Germline variants establish life-course risk and recalibrate surveillance intensity. Somatic mutational signatures and field cancerization describe spatial conditioning of tissues long before overt malignancy. Circulating tumor DNA (ctDNA) provides temporal resolution by tracking clonal persistence after therapy. Extending this framework, emerging evidence from microbiome and metabolomic studies supports the hypothesis that sustained alterations in volatile organic compound (VOC) profiles may reflect early tumor–microbiome ecosystem shifts. Although a comprehensive wearable multi-gas detection device is not yet clinically available, current technological advances render continuous “volatomics” biologically plausible and conceptually aligned with trajectory-based monitoring. Rather than advocating a single transformative assay, this manuscript argues for convergence: longitudinal biomarker baselines, germline priors, tumor-informed molecular templates, ctDNA dynamics, and prospective metabolic sensing integrated within a calibrated decision system. Such a platform would function not as a replacement for established diagnostic tools, but as a stratified triage architecture capable of identifying sustained biological deviation warranting further evaluation. Early detection, in this reframed paradigm, becomes a dynamic process of recognizing evolving biological drift rather than a binary event triggered by threshold crossing.

Abstract: The Box–Cox transformation is widely used to induce approximate normality and linearity in statistical modelling. Within the Power Normal framework, it embeds non-Gaussian variables into a latent Gaussian structure where conditional relationships become linear. However, the inverse transformation does not generally preserve these functional relationships when returning to the original scale. In this paper, we formally analyze the discrepancy between the inverse image of the linear regression function in the transformed domain and the true conditional expectation in the original scale. We derive an explicit second-order decomposition showing that the conditional mean in the original scale consists of the inverse-transformed linear predictor plus a curvature-induced correction term proportional to the conditional variance. This distortion term depends explicitly on the transformation parameter and the local geometry of the inverse Box-Cox function. The analysis reveals that the loss of structural preservation under inversion is an intrinsic consequence of the nonlinear transformation and can be interpreted as a second-order Jensen-type correction. Numerical illustrations based on simulated bivariate Power Normal models confirm the theoretical findings. These results clarify a structural limitation of transformation-based Gaussian modelling and provide insight into its implications for statistical inference and applied modelling.

Abstract: AbstractBruceine A is a major quassinoid isolated from the seeds of Brucea javanica and has been reported to exhibit significant anticancer and antiplasmodial activities. Structural modification of Bruceine-A through semisynthesis is a rational approach to improve its biological potential. This study aimed to design and evaluate semisynthesized pathways for Bruceine-A derivatives and to elucidate the reaction mechanism. Two semisynthesis routes were evaluated: (i) a protection–deprotection strategy involving tert-butyldimethylsilyl chloride (TBDMS-Cl), and (ii) a direct acylation approach. Due to limitations in material availability and reaction complexity, the second pathway was selected. Direct acylation of Bruceine A in N,N-dimethylformamide (DMF) using imidazole as a base catalyst successfully yielded 3-O-chlorobenzoylbruceine (P1). Structural elucidation was performed using UV, IR, ^1H-NMR, ^13C-NMR, and LC–MS. The results demonstrate that direct acylation at the C-3 hydroxyl group is an efficient and selective strategy for the semisynthesis of Bruceine A derivatives.Keywords: Bruceine A, quassinoid, semisynthesis, acylation, Brucea javanica, 3-O-chlorobenzoylbruceine

Abstract: The article explains the narcissism leadership paradox in the existing organizations in relation to the rhetoric of ethics used strategically to legitimize the use of control. The loss of trust in leaders and in employees are both practiced in the sense that leaders manifest the disjunction between organizational discourses and reality by instantiating values in superficial ways in what they say and in real ways in what they do. The study relies on three guiding questions: (1) How do narcissistic leaders legitimize themselves by thinking that they are right in the moral sense? (2) What are a few of the stressors related to employees where ethics and practice collide? (3) Does dissonance cause organizational cynicism? Semi-structured interviews with 24 employees working in Higher Education Institutes were used to collect qualitative data to answer the following questions: The similar patterns and their comparison across cases were determined by coding and performing thematic analysis in computer through excel. The outcomes show 3 broad themes. First, the Virtue Costume demonstrates that both virtues signaling and moral language are being offered to fulfill personal interest and acquire power. Second, Branding the Self as the Company causes us to concentrate on how egoistic leaders project their own image as the identity and values of the company. Third, the Contagion of Cynicism explains how employees who become disillusioned, cynical and detached respond when they feel hypocrisy in the words and actions of their leaders. The paper associate’s impression management and moral justification of narcissist leaders with falling trust and calls on authentic leadership and open cultural supervision to restrain cynicism and provide theoretical and practical organizational knowledge. This study’s implications build on the dark triad perspective advanced by Alowais and Suliman, which demonstrated that Leader Dark Triad (LDT) traits can cascade into Employee Dark Triad (EDT) behaviors within organizational settings. Extending this logic, the present study shows that narcissistic leaders’ ethical rhetoric can similarly shape organizational climates in ways that reinforce manipulative dynamics, highlighting how seemingly ethical leadership signals may mask deeper patterns of influence and behavioral contagion.

Abstract: Previous studies suggest that elevated competitive anxiety may increase the likeli-hood of injury. The present research aims to examine the role of competitive anxiety as a predictor of injury occurrence, frequency, and severity. A cross-sectional, correlational de-sign was conducted with 131 athletes, (mean age = 16.49 years), predominantly male. In-juries data were obtained through medical record review, and competitive anxiety was assessed using the Competitive Anxiety Inventory-2. Empirical frequency distributions, descriptive statistics, non-parametric tests, and logistic and ordinal regression models were employed. A high incidence of injuries was observed, although most were minor. Competitive anxiety was characterized by elevated levels of cognitive anxiety and self-confidence. Injured athletes exhibited greater overall competitive anxiety (r = .31, p < .001), with higher levels observed among those who sustained more injuries (ε² = .12, p = .001), and a very large effect was found in relation to injury severity (ε² = .17, p < .001). The occurrence of injury can only be predicted in 10.9–14.7% of cases through increased cogni-tive and somatic anxiety, whereas an increase across all dimensions of competitive anxi-ety predicts a greater number (13–14%) and severity (20.3–21.8%) of injuries. These find-ings underscore the importance of developing skills to manage competitive anxiety, par-ticularly its cognitive dimension and maintaining optimal levels of self-confidence in young athletes.

Abstract: Blast furnace gas (BFG) must be deeply purified when it is as fuel for combined-cycle power generation. To improve collection efficiency of the fine particulate dust in BFG by wet electrostatic precipitators (WESPs), this study implemented measures such as optimizing nozzle atomization performance and spatial distribution of droplets, along with adding chemical agglomeration agents and surfactants, These approaches pro-moted the chemical agglomeration of fine dust and enhanced dust collection efficiency. The results show that under overlapping spray conditions, the 1/8 solid cone nozzle produced the smallest droplets size with the most uniform spatial distribution, exhib-iting a d50 of 141.17 μm. When this nozzle was used in combination with guar gum (GG) as a chemical agglomerant, the d50 of BFG dust increased from 8.46 μm to 14.75 μm. The synergistic application of 5 mg/m³ sesbania gum (SBG) and 5 mg/m³ oc-tylphenol ethoxylate (OP-10) further increased the dust d50 to 19.08 μm. Using the 1/8 solid cone nozzle and with an XTG concentration of 5 mg/m³, resulted in the highest dust collection efficiency of 96.76%, while the synergistic use of SBG/OP-10 achieved an efficiency of 97.69%. This study elucidates the influence of nozzle atomization charac-teristics and spray liquid type on dust agglomeration and collection efficiency, providing both theoretical and practical foundations for the deep purification of blast furnace gas.

Abstract: Oculomics represents a paradigm shift in medicine, redefining the eye as a non-invasive window into systemic health rather than merely a target of disease. This emerging interdisciplinary field leverages high-resolution ocular imaging—including fundus photography, optical coherence tomography (OCT), and OCT angiography—along with molecular analysis of ocular biofluids to identify biomarkers of cardiovascular, metabolic, neurodegenerative, renal, and environmental diseases. Grounded in the retina’s shared embryological, neurovascular, and metabolic pathways with the brain and systemic vasculature, oculomics enables the detection of subclinical pathological processes often years before overt clinical manifestations. The integration of artificial intelligence, particularly deep learning, has been instrumental in decoding complex, high-dimensional ocular data, transforming routine eye examinations into scalable platforms for predictive risk stratification and personalized medicine. Unlike prior reviews focused on technological implementation or clinical integration, this work provides a mechanistic, disease-centric synthesis that maps quantitative retinal and tear-fluid biomarkers to underlying systemic pathophysiology, offering a granular blueprint for future translational research. This review synthesizes the biological rationale, key technologies, and disease-specific evidence underpinning oculomics, while critically examining its translational framework—termed “Healthcare from the Eye.” We also address persistent challenges related to standardization, clinical validation, ethical governance, and health system integration. As these barriers are addressed, oculomics is poised to reposition ophthalmology at the forefront of preventive and precision medicine, making routine eye care a gateway to early systemic health assessment and intervention.

Abstract: The block-and-lock strategy aims to achieve a functional cure for human immunodeficiency virus type 1 (HIV-1) infection by enforcing durable, drug-independent silencing of proviral transcription. Several latency-promoting agents have been described that effectively limit viral reactivation in vitro or in animal models. However, most approaches induce only partial or reversible transcriptional repression and have not yet been translated into safe and effective clinical interventions. This review summarizes the molecular mechanisms underlying block-and-lock strategies and critically evaluates the limitations of current candidate compounds. We highlight recent advances in understanding HIV-1 integration site selection, focusing on the roles of lens epithelium-derived growth factor p75 (LEDGF/p75) and cleavage and polyadenylation specificity factor subunit 6 (CPSF6) in directing proviral integration toward gene-dense, transcriptionally active chromatin. Pharmacological disruption of the LEDGF/p75–integrase interaction by LEDGF/p75 inhibitors (LEDGINs) redirects proviral integration toward less transcriptionally active genomic regions that are more resistant to reactivation. Recent tandem knockout studies, however, demonstrate that CPSF6 plays a dominant role in guiding HIV-1 integration toward gene-dense, transcriptionally active chromatin. LEDGIN treatment has been linked to the preferential targeting of proviruses to heterochromatin-rich regions within the nuclear interior. By contrast, CPSF6 knockout redirects integration toward peripheral heterochromatin, especially lamina-associated domains (LADs), genomic regions typically exhibiting stronger and more stable transcriptional repression than interior heterochromatin. These findings suggest that therapeutic modulation of CPSF6 may exert a more profound and durable effect on proviral silencing within a block-and-lock framework. Nevertheless, complete CPSF6 ablation is associated with severe cellular toxicity. The challenges associated with CPSF6-related adverse effects and potential strategies to overcome these limitations are discussed.

Abstract: Machine learning capabilities are expanding into scientific domains at an accelerating pace. When applied to high energy physics pattern discovery, they will generate candidates faster than traditional evaluation can absorb. ML finds patterns in past data. It is inherently post hoc. Whether those patterns reflect structure or coincidence is unknowable at discovery time. This limitation applies equally to human and computational pattern finding. What differs is scale. ML candidate generation is effectively unbounded, while human evaluation capacity remains fixed. When generation rate exceeds evaluation bandwidth, binary accept or reject degenerates to random sampling. Information theoretically, the only response that preserves ranking under a finite evaluation budget is stratification. By focusing on stratification rather than binary filtering, rule adjustments can be made retroactively, thresholds tuned as results accumulate, and evaluation bandwidth focused on top ranked candidates. This paper attempts to codify those criteria, proposing seven computationally evaluable standards for stratifying ML generated patterns. The goal is not to deliver verdicts but to prioritize which candidates merit preregistration and longitudinal tracking. The framework preserves the essential paradigm: pattern plus theory equals potentially real physics. Patterns alone, however striking, remain candidates until theoretical understanding arrives. Making these criteria explicit enables prefiltering at scale while creating a collaborative resource rather than a competitive one. ML capabilities extend what physicists can search while preserving how physicists evaluate. We offer this provisional framework for community calibration, with the goal of developing validation infrastructure before the capability fully arrives.

Abstract: Background: Drug-resistant tuberculosis (DR-TB) remains a major public health challenge in South Africa, particularly in rural settings with high HIV co-infection rates. Under-standing predictors of treatment response among people living with HIV is essential for improving clinical management and programmatic outcomes. This study aimed to iden-tify socio-demographic and clinical predictors of treatment outcomes among HIV-positive individuals diagnosed with multidrug-resistant (MDR) and extensively drug-resistant tuberculosis (XDR-TB) in the rural Eastern Cape Province, South Africa. Methods: A ret-rospective cohort study was conducted using routinely collected clinical records of DR-TB patients initiated on treatment between January 2020 and December 2024 at two public healthcare facilities. A total of 239 patients with complete treatment outcome data were included. Treatment outcomes were classified as favorable (cured or treatment completed) or unfavorable (death, treatment failure, or loss to follow-up). Descriptive statistics were used to summaries patient characteristics, while univariate and multivariable logistic re-gression analyses were performed to identify factors associated with treatment outcomes. Results: Most participants were aged ≤39 years (58%), male (60%), unemployed (90%), and without income (80%). MDR-TB accounted for 40% of cases, rifampicin-resistant TB (RR-TB) for 53%, and XDR-TB for 7.1%. Multivariable analysis showed that XDR-TB was the strongest independent predictor of unfavorable treatment outcome (AOR = 0.18; 95% CI: 0.06–0.58; p = 0.004). Income status was also significantly associated with outcome, with participants reporting some income having lower odds of favorable outcomes (AOR = 0.46; 95% CI: 0.23–0.92; p = 0.036). The model demonstrated modest predictive perfor-mance (AUC = 0.67). Conclusion: These findings highlight the dominant influence of re-sistance phenotype particularly XDR-TB on treatment prognosis among HIV-positive DR-TB patients in rural Eastern Cape. Integrating early resistance profiling, intensified clinical management of XDR-TB, and socioeconomic support mechanisms may improve treatment outcomes in high-burden rural settings.

Abstract: Cultural-heritage destinations are adopting digital twins and Living Labs to improve conservation, safety, and visitor experience. Operationalising these initiatives requires trustworthy interfaces capable of answering questions grounded in authoritative sources under public-sector governance constraints. We present a Sovereign Conversational Assistant (SCA) based on a small-language-model (SLM) plus retrieval-augmented generation (RAG) platform designed for the next generation Libelium Heritage Living Lab. This assistant is, therefore, agnostic from any specific LLM. Testing has focused on the usage of newly released, Barcelona Supercomputing Center’s own BSC-LT/ALIA-40b-instruct-2601 as well as the mistralai/Mistral-Small-3.2-24B-Instruct-2506, one of the SoTA standard bearer for mid-size SLMs. It integrates provenance logging, safety controls, and language enforcement. We evaluate the assistant benchmark on 19 tests across five categories: historical queries, client experience, data analysis, hallucination resistance, and safety/ethics. Our findings reveal that while both models adeptly retrieve factual historical and operational information, their reliability diverges under complex conditions. Mistral achieved a 100% pass rate across all tests, demonstrating strong analytical capabilities without hallucination and keeping up with the multilingual and safety guardrails, too. In contrast, ALIA struggled with numerical values drifting during data analysis and exhibited vulnerabilities in cross-language scenarios. The results show that a compact, sovereign RAG stack running on ALIA can meet core information needs in English and Spanish for Heritage Living Labs, while highlighting the necessity of refusal robustness and explicit multilingual control for public-facing deployment.

Abstract: Introduction: Multimorbidity, the coexistence of two or more chronic conditions, is a major global public health challenge and exists on a continuum from non-complex to highly complex, with increasing complexity and multi-system involvement linked to greater instrumental activities of daily living (IADL) disability, frailty, and mortality. Objectives: This study aims (1) to assess chronic condition complexity (CCC) levels by distinguishing multiple chronic conditions (MCCs) from multimorbidity (MM) among older adults in Albania; (2) evaluate their prevalence and impact on care needs, medication adherence, and quality of life; and (3) examine associations between MM and sociodemographic factors, including age, gender, educational level, and medication burden. Methods: An observational, descriptive, cross-sectional, multicenter study with analytical approach was conducted among older adults aged 65 years and older with 2 or more chronic conditions in Albania. Data were collected from participants attending six Primary Health Care Centers located in South of Albania, between March and December 2024. Data were collected using the Simplified Medication Adherence Questionnaire (SMAQ) and the Older People's Quality of Life Questionnaire (OPQOL-35), both of which are validated and reliable tools for the Albanian population. Results: Diagnosis status was significantly associated with age, educational attainment, and medication burden, with older adults experiencing MM more likely to be older, have lower education, use multiple medications, rely on family for care, and exhibit lower medication adherence, underscoring the influence of sociodemographic factors and treatment burden on the health outcomes of older adults.

Abstract: Metro Manila, one of the world’s most densely populated megacities, is highly vulnerable to sea-level rise due to its low-lying deltaic location, frequent tropical cyclones, and rapid anthropogenic subsidence from groundwater extraction. This study integrates historical PSMSL tide-gauge records from Manila Harbour with IPCC AR6 projections under Shared Socioeconomic Pathways, incorporating vertical land motion (VLM) and sea-level fingerprints to estimate local relative sea-level (RSL) changes. Assuming constant subsidence, cumulative VLM reaches –0.785 m by 2100 and –1.289 m by 2150. Including climatic contributions (amplified 10–20% by fingerprints, particularly under high-emission scenarios from far-field Antarctic ice loss in the western Pacific), projected RSL ranges from 1.09–1.42 m (SSP1-2.6) to 1.51–2.00 m (SSP5-8.5) by 2100, and from 1.70–2.28 m to 2.41–3.54 m by 2150. Results indicate 7.95–11.15 km² (1.2–1.8%) of land could face permanent ocean-connected inundation under high scenarios, mainly in Malabon (~18%), Navotas (~20%), and Manila (~7%). These conservative estimates (excluding aquaculture areas) are much lower than prior mid-century projections of up to 30%. Intensified chronic flooding, erosion, and saltwater intrusion threaten millions, requiring urgent integrated adaptation, groundwater regulation, and combined nature-based and engineered solutions.

Abstract: Urban regeneration frequently encounters a critical trade-off: whether to accelerate planning and implementation of design solutions or safeguard participation. To address this challenge, the paper introduces the concept of the governance “grey zone”—an informal yet institutional interface that flexibly reconfigures the relationship between planning and design to transcend the impasse. This perspective is grounded in an analysis of the recent urban regeneration of Hsinchu City, where a weekly, mayor-led coordination forum with external consultants functioned as an informal yet institutional organizational hub. This forum broke down departmental silos, unified multiple design teams under shared principles, and expedited implementation of numerous projects—all while maintaining public scrutiny and inclusivity. The study draws on interviews with high-profile administrators, planners, and designers involved in Hsinchu’s regeneration, as well as official documents. Elaborating on this, the paper finally advances a set of implications regarding urban regeneration scholarship with attention to aspects of urban design governance.

Abstract: Efficient calibration is essential for the practical application of option pricing models. The Fractional Stochastic Volatility Jump Diffusion (FVSJ) model proposed by Zhang and Yong [1] can reproduce several stylized features observed in option markets, including the volatility smile, volatility clustering, and long-memory effects. However, its multiple stochastic components make conventional calibration computationally expensive. This paper proposes a two-step calibration framework that combines a neural network with a differential evolution (DE) algorithm. In the first step, we construct a Physics-Informed Kolmogorov-Arnold Network (PCKAN) to approximate the FVSJ pricing map. Specifically, we replace the B-spline basis in KAN with second-kind Chebyshev polynomials and incorporate a Black-Scholes PDE residual as an additional penalty term in the training objective, aiming to improve global approximation and enhance numerical stability and interpretability. In the second step, the trained PCKAN is used as a fast surrogate pricer within the DE algorithm to accelerate parameter estimation. Empirical results show that the proposed method achieves calibration accuracy comparable to direct pricing while substantially reducing computational time.

Abstract: In this paper, we consider the properties of the following objects: plafal and geo-space (a general overview). As an application of the created theory, the proof of the equality of complexity classes P and NP will be given.

Abstract: Advanced economies face a compounding demographic crisis: populations aged 65 and over will reach 30–40% in several nations by 2050, ageing-related expenditure already absorbs up to 18% of GDP in the most affected economies, and demographic ageing is projected to reduce annual GDP growth by 0.3–1.2 percentage points by 2035. Conventional policy instruments have failed to resolve pressures that include severe long-term care workforce shortfalls across leading ageing economies and per-capita elderly care costs running 3–5 times those of working-age cohorts. This structured narrative review of 81 sources (2020–2025) evaluates whether Agentic AI defined as autonomous, goal-directed systems capable of multi-step workflow coordination can support structural adaptation in ageing health systems. A consistent finding is that implementation outcomes are determined by institutional conditions rather than algorithmic performance, and evidence strength is inversely correlated with intervention complexity. Three contributions are presented: the Agentic AI Framework (AAF 3.0); a cross-domain synthesis formalising the inverse evidence–complexity relationship; and a phased sociotechnical roadmap integrating governance sequencing, reimbursement reform, and equity safeguards. Short-term productivity gains are documented; macroeconomic fiscal moderation remains empirically unvalidated.