Opportunistic osteoporosis screening using artificial intelligence (AI) applied to routine chest radiographs offers a novel, low-burden strategy to improve early detection and treatment initiation. This study evaluated the costeffectiveness of AI-enhanced opportunistic osteoporosis screening compared with no screening among Australian women aged >= 50 years undergoing chest radiography. A microsimulation-based Markov model was developed to estimate quality-adjusted life years (QALYs) and lifetime costs in 2025 Australian dollars. The model incorporated the diagnostic performance of AI applied to chest radiographs, subsequent DXA confirmation, treatment initiation, and real-world medication persistence. It also explored the effect of enhanced follow-up and adherence on clinical and economic outcomes. AI-enhanced opportunistic screening was associated with improved health outcomes (+0.0025 QALYs and 0.0043 fractures per patient) and lower lifetime costs (-AUD59) compared with no screening, representing a dominant strategy. When scaled to 10,000 screened women aged >= 50 years, the model projected prevention of 43 fractures, 25 additional life-years, 25 QALYs gained, and AUD590,000 in healthcare savings. Optimizing follow-up and adherence could amplify clinical benefits by four- to five-fold. AI-enhanced opportunistic osteoporosis screening from chest X-rays appears to be a cost-effective strategy within the Australian healthcare system, delivering better outcomes at lower cost. The results also suggest that linkage to DXA confirmation and subsequent treatment initiation may influence the magnitude of health and economic outcomes. These findings highlight that follow-up and persistence are not merely complementary but essential for realizing the full potential of cost-effective AI-driven osteoporosis screening.
Objective The current lack of an unambiguous and widely applicable quality indicator for epilepsy care could hamper epilepsy care. The main aim of this systematic review is to study patient-reported outcome measures (PROMs) instruments that evaluate the quality of epilepsy care and inventorize the main themes covered as well as the measurement properties of these instruments. Methods The protocol was registered in PROSPERO (ID: CRD42025637902) and reporting followed the PRISMA reporting guidelines for systematic reviews. Electronic databases searches were conducted on PubMed, Embase and Web of Science. Articles published up to 2025 were considered. Dutch or English-language articles with a focus on the development and validation of PROMs to evaluate epilepsy care were included. Reviews, editorials, conference abstracts, protocols and translations were excluded. Covered themes and measuring properties (validity, external reliability, internal reliability and readability) were extracted. Risk of bias assessment was performed using ‘Consensus-based Standards for the selection of health measurement instruments’ (COSMIN) guidelines. Results The search resulted in 1,904 articles. Seven articles met our inclusion criteria, evaluating a total of seven instruments. Recurring themes included seizure control (n = 5), treatment (n = 5), daily life (n = 3), psychosocial functioning (n = 3). Less frequently addressed themes included diagnostics (n = 2), quality of life (n = 2), injury (n = 2), reproductive health (n = 1), sleep (n = 1) and informal caring (n = 1). Significance Most instruments evaluating epilepsy care focus on seizure control and treatment aspects while other domains like daily activities and psychological functioning are less well covered. Including these domains is however advisable as they are important components of QoL.
Chemical interfaces involve a hierarchy of coupled phenomena spanning quantum confinement, interfacial geometry, electrostatic fields, molecular interactions, statistical mechanics, and macroscopic thermodynamics. Establishing a consistent connection among these levels remains a fundamental challenge in theoretical interface science. This work develops a multiscale Hamiltonian framework in which the quantum state of an interfacial system is formulated on a curved geometry and coupled to electrostatic, intermolecular, and thermodynamic degrees of freedom. Surface geometry enters the quantum description through the Laplace–Beltrami operator and the curvature-induced geometrical potential, while electrostatic interactions are described through a self-consistent Schrödinger–Poisson formulation extended to electrolyte-containing interfaces. London dispersion, specific Lewis acid–base interactions, residual polarization, exchange repulsion, and lateral interactions are incorporated as physically differentiated contributions, with an explicit reference-plus-residual construction introduced to prevent double counting between continuum electrostatics and additional interaction terms.A central distinction is established between fundamental quantum operators and experimentally parameterized coarse-grained interactions. In particular, Gutmann donor and acceptor descriptors retain their thermodynamic status, while the generalized five-parameter Lewis acid–base model provides an experimentally constrained effective contribution rather than a newly postulated microscopic quantum observable. Statistical mechanics subsequently connects the resulting interfacial energetics to finite-temperature ensemble properties and thermodynamic observables.The coupled equations are formulated within an explicit self-consistent computational architecture linking the quantum state, charge density, electrostatic potential, ionic distributions, geometry, and thermodynamic variables. Quantitative curvature analysis identifies the regimes in which geometrical quantum effects and electrostatic curvature become significant through their characteristic dependence on interfacial radius, particle mass, thermal energy, and Debye screening length. Computational tractability is further demonstrated through a self-consistent cylindrical Schrödinger–Poisson benchmark, for which electrostatic feedback produces a 9.58 meV shift in the lowest quantum level, compared with a −1.27 meV curvature-induced geometrical contribution for the representative nanoscale system considered.The framework recovers established planar, weak-coupling, and classical limits where the corresponding contributions become negligible. Experimental results from inverse gas chromatography, surface energetics, adsorption thermodynamics, and electrokinetic measurements provide independent constraints on the physical interaction sectors represented in the formulation. The resulting framework does not replace the established theories governing these individual regimes; rather, it provides a common multiscale architecture for connecting interfacial geometry, quantum states, physicochemical interactions, self-consistent electrostatics, and finite-temperature thermodynamics within explicitly stated assumptions and levels of description.
Innovation research has long shown that firms depend on their R&D personnel to strengthen their innovation capabilities, sparking scholarly interest in how to best staff R&D departments for competitive advantage, with a focus on gender diversity. However, left unexamined are the mechanisms linking gender diversity in R&D departments and firm-level innovation. Further, despite growing interest in the conditions under which diversity may enhance innovation, researchers have not adequately addressed how department-level gender rank inequality-the disproportionate concentration of men and women at various hierarchical ranks-interacts with numerical diversity to affect innovation outcomes. We address both limitations by integrating insights from the information/decision-making perspective on diversity and research on inequality with research on innovation capabilities. Specifically, we develop and test a model explaining how R&D department gender diversity and gender rank equality jointly affect three firm-level innovation capabilities: breadth of external knowledge search, use of external knowledge, and quality of the innovation implementation process. We also examine the indirect effects of gender diversity on firm-level innovation, conditional on gender rank equality, via these capabilities. Our analyses of 552 corporate R&D departments over 12 years show how gender diversity and gender rank equality in R&D departments jointly relate to higher levels of all three firm-level innovation capabilities and, indirectly, to greater firm-level innovation. Our findings suggest that improving a firm's innovation capabilities and performance requires both increased representation of women in R&D departments and proportional representation of men and women at all ranks of R&D departments.
