Cardinal Stefan Wyszyński University in Warsaw (UKSW) (Latin: Universitas Cardinalis Stephani Wyszyński Varsoviae) – a Polish state university created on the basis of the Academy of Catholic Theology in Warsaw. UKSW is a public university that offers education in various fields of study: from humanities and social studies to exact and natural sciences, and since 2019 also medicine.The University has 12 faculties located in two campuses in Warsaw's Bielany district: on Dewajtis and Wóycickiego Streets. Students can choose from more than 40 majors, including medicine, psychology, law, journalism, environmental engineering, Italian Philology, and economics.In recent years, the university has been transforming itself into a modern science research center. In 2016, Mazovian Laboratory Center of Life Sciences UKSW was established on the campus at the Wóycickiego Street. Practical classes for science and medical students take place in dozens of research laboratories and conceptual work rooms. The University also has, among other things, a modern sports center, a radio and television studio and a passenger car simulator for research at the Institute of Psychology.In 2019, it received the European Commission's "HR Excellence in Research" award, confirming its adherence to the principles of the European Charter for Researchers. In addition, all faculties of the university are under the supervision of the Minister of Science and Higher Education, four of them (Faculty of Theology, Faculty of Christian Philosophy, Faculty of Canon Law and Faculty of Family Studies) are additionally supervised by church authorities.
Public announcements of robot purchases are often treated as equivalent to new robotic-urology capacity, although procurement disclosure and stable public activity are different milestones. We assessed whether contract architecture helps interpret public activation of robotic urology programs in Poland. We conducted a nationwide open-data linkage study linking the CEZ/NFZ provider-level reimbursement workbook for robotic surgery in Poland (April 2022-June 2025) with auditable Polish da Vinci disclosures (February 2024-March 2026), an exact-PDF subset, and a fixed public Synektik page naming 42 hospitals. First public reimbursement was treated as a proxy for first publicly visible activation. Analyses included segmented diffusion modelling, rolling-origin validation, trajectory clustering, and exploratory contract-architecture clustering. Poland had 56 providers with any public robotic surgery and 53 with public RARP across 14 regions. Median first-month public RARP volume was 6 cases and median first-6-month volume was 54. A segmented Poisson model outperformed a single-trend model (AIC 119.6 vs. 138.1), with the best breakpoint in November 2022 and 24.3
BACKGROUND Missed appointments (patient no-shows) are a critical challenge undermining healthcare system efficiency globally. This study aims to characterize the patient no-show phenomenon in Poland, identify factors associated with missed appointments, and propose potential measures to reduce the no-show phenomenon in the Polish healthcare system. MATERIAL AND METHODS A nationwide cross-sectional survey was conducted using computer-assisted web interviews (CAWI) from August 1 to 4, 2025. The study used quota sampling stratified by sex, age, and residence to obtain a nationwide sample of 1162 Polish adults aged 18 to 96 years. A self-prepared questionnaire was used. RESULTS Among all respondents, 88.5% used healthcare services within the previous 12 months. Among healthcare users (n=1014), 14% missed appointments without cancellation. Forgetting appointments (42.3%) and communication barriers (27.5%) were identified as the primary reasons for no-shows. Text message (SMS) reminder systems received 62.5% support, while 67.5% endorsed the implementation of a penalty fee for public system non-attendance. Multivariable analysis revealed significantly (P<0.05) increased odds of no-shows among adults aged under 60 years of age, parents with children <18 years (aOR, 2.09; 95% CI, 1.28-3.40), and individuals with moderate (aOR, 1.80; 95% CI, 1.19-2.72) or poor financial status (aOR, 2.60; 95% CI, 1.47-4.60). CONCLUSIONS This study showed a relatively high prevalence of missed appointments in Poland. Young age, parental responsibilities, and economic constraints were associated with higher odds of no-shows. Findings support expanding digital notification systems and multi-channel communication infrastructure to reduce no-shows, rather than using punitive approaches.
Robot-assisted kidney transplantation (RAKT) converts vascular anastomosis time into an incompletely measured thermal exposure. We mapped how grafts are cooled and how temperature is measured, reported, and linked to outcomes during RAKT. Following a prespecified protocol and a documented search-source amendment, PubMed/MEDLINE, Scopus, and Web of Science Core Collection were searched from inception to 26 July 2026; a backward and forward citation-chain audit was completed on 2 August 2026. Human or large-animal RAKT reports were eligible if they provided numeric intraoperative graft or pelvic thermometry or evaluated a purpose-built cooling system. Data were charted by report and innovation program. Clinical, technical, and measurement heterogeneity precluded meta-analysis. Reporting followed PRISMA-ScR. Seven reports from six innovation programs met eligibility criteria: five human studies, one porcine study, and one mixed bench–porcine–human development study. Six reports contained numeric graft or pelvic thermometry; one mesh-wrap report specified a target below 15 °C but did not measure graft temperature. Among continuous-cooling RAKT groups reporting mean ± SD thermometry, values ranged from 6.5 ± 3.1 °C at reperfusion in pigs to 19.74 ± 1.61 °C at 65 min in a matched human cohort; regional ice-based clinical series reported mean pre-reperfusion surface temperatures of 20.1–22.5 °C. Only two reports explicitly specified both a precise anatomical measurement site and a sampling interval.No study validated a clinically actionable threshold or an adjusted temperature–outcome relationship. Observed thermometry in six reports demonstrates that hypothermic conditions can be achieved under specific cooled RAKT protocols, but evidence of target attainment does not extend to the device-intent-only report and comparative clinical effectiveness remains unproven. Elapsed rewarming time should not substitute for graft thermal exposure. Standardized sensor metadata, serial temperatures, and temperature-dose metrics are prerequisites for comparative trials.
Urolithiasis management increasingly depends on accurate, noninvasive stone phenotyping to guide acute intervention, secondary prevention, and selective chemolitholysis. Photon-counting computed tomography (PCCT) introduces detector-level energy discrimination and higher spatial resolution, enabling calcium-preserving reconstruction strategies and quantitative spectral analytics that may shift stone characterization from a laboratory endpoint toward an imaging-derived biomarker. Recent peer-reviewed PCCT studies have concentrated on three translational domains. First, calcium-preserving virtual non-iodine (VNI) and virtual non-contrast (VNC) reconstructions have been evaluated for upper-tract stone detection in contrast-enhanced settings, supporting the concept that a single contrast-enhanced acquisition could potentially replace multiphase protocols in selected scenarios. Second, comparative ex vivo and clinical imaging studies suggest that PCCT improves depiction of small calculi and enables automated, high-resolution stone burden quantification. Third, spectral radiomics and machine-learning models have been applied to monoenergetic PCCT reconstructions for multi-class stone composition discrimination, achieving high discriminatory performance in controlled ex vivo datasets, and complementary phantom work has demonstrated automated uric acid versus non-uric acid classification. The emerging literature suggests that PCCT may support calcium-preserving assessment of stones in contrast-enhanced imaging, automated and reproducible stone burden quantification, and composition phenotyping via spectral analytics. However, most studies remain phantom/ex vivo and highly platform-specific. Translation will depend on prospectively defined acquisition and reconstruction parameters, externally validated models, and rigorous reporting aligned with contemporary machine-learning standards.
