The Government of Hungary (Hungarian: Magyarország Kormánya) exercises executive power in Hungary. It is led by the Prime Minister, and is composed of various ministers. It is the principal organ of public administration. The Prime Minister (miniszterelnök) is elected by the National Assembly and serves as the head of government and exercises executive power. The Prime Minister is the leader of the party with the most seats in parliament. The Prime Minister selects Cabinet ministers and has the exclusive right to dismiss them. Cabinet nominees must appear before consultative open hearings before one or more parliamentary committees, survive a vote in the National Assembly, and be formally approved by the President. The cabinet is responsible to the parliament.Since the fall of communism, Hungary has a multi-party system. A new Hungarian parliament was elected on 8 April 2018. This parliamentary election was the 8th since the 1990 first multi-party election. The result was a victory for Fidesz–KDNP alliance, preserving its two-thirds majority with Viktor Orbán remaining Prime Minister. It was the second election according to the new Constitution of Hungary which went into force on 1 January 2012. The new electoral law also entered into force that day. The voters elected 199 MPs instead of previous 386 lawmakers.
The global development discourse primarily emphasizes the vital role of international aid in post-conflict health systems and governance. Somalia’s post-conflict health recovery has relied heavily on multisectoral aid that saved lives but entrenched parallel systems. While the national budget rose from SOS 200 million (2015) to SOS 1.3 billion (2025), the Ministry of Health’s share remained ≤ 7
Introduction: Achieving viral suppression is central to HIV epidemic control; however, routine viral load (VL) testing in many low- and middle-income countries is constrained by laboratory capacity, logistics, and cost. In Tanzania, disparities in VL coverage persist across age groups and geographical regions, limiting the timely detection of treatment failure. Pharmacy refill adherence is a low-cost, routinely collected objective indicator of treatment behavior. This study assessed whether pharmacy refill adherence, enhanced using machine learning (ML) models, can reliably predict viral suppression among people living with HIV (PLHIV) in Tanzania. Methods: We conducted a retrospective analysis using nationally representative patient-level data from the Care and Treatment Center (CTC-2) database, collected between 2017 and 2021. A random sample of 40,000 records was drawn, of which 28,044 patients met the inclusion criteria. Pharmacy refill adherence was calculated as the proportion of days covered and capped at 100%. Viral suppression was defined as a VL of <1,000 copies/mL. Logistic regression, Random Forest, Gradient Boosting Machine (GBM), and XGBoost models were trained using an 80/20 training-testing split, and the model performance was evaluated using the area under the receiver operating characteristic curve (AUC). Youden's Index was used to determine the optimal adherence threshold. Results: Among the 28,044 patients included in the analysis, the median age at ART initiation was 38 years, and 64.9% were female. The median pharmacy refill adherence was 90.64% (mean, 87.37%). Viral load (VL) measurements were available for 21,572 patients, of whom 88.7% achieved viral suppression. Higher pharmacy refill adherence was strongly associated with viral suppression, whereas lower adherence was observed among adolescents, young adults, and individuals who were lost to follow-up. Marked geographic variation was observed, with higher adherence in regions such as Dar es Salaam and lower adherence in more remote regions, including Rukwa and Singida. Among machine learning models, XGBoost demonstrated the highest predictive performance (AUC >0.85), followed by Gradient Boosting Machines and Random Forest, while logistic regression provided stable baseline estimates. Pharmacy refill adherence, duration of follow-up, clinic visit frequency, and patient age were the strongest predictors of viral suppression. Conclusion: Pharmacy refill adherence is a strong predictor of viral suppression and provides a feasible and cost-effective tool for monitoring ART outcomes in settings with limited VL testing. Machine learning approaches further enhance the predictive value of routine program data and can support the early identification of patients at risk of virological failure. Integrating adherence-based predictive analytics into national HIV program monitoring systems may strengthen differentiated service delivery, improve treatment outcomes, and accelerate progress toward the UNAIDS 95-95-95 targets in Tanzania and similar resource-limited settings. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement Meshack D. Lugoba received a small Sida-supported grant administered through Muhimbili University of Health and Allied Sciences (MUHAS), which contributed to capacity building but did not influence the study design, data analysis, interpretation of results, or the decision to publish. Data access and institutional support were provided by the National AIDS and Sexually Transmitted Infections Control Programme (NASHCOP) and MUHAS. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethical clearance for this study was obtained from the Directorate of Research and Publication of the Muhimbili University of Health and Allied Sciences (MUHAS) with reference number DA.282/298/01.C/. The NASHCOP administration granted permission to collect the data. Strict privacy and confidentiality were maintained throughout the study. Only de-identified data were used, and patient IDs, names, or other personally identifiable information were not collected from the patients. Data collection adhered to national and international ethical standards, particularly regarding the handling of sensitive health information. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The Government of Tanzania owns the data used in this study through NASHCOP, and they are not publicly available. The de-identified analytical code and derived output can be made available from the corresponding author upon reasonable request and subject to NASHCOP approval.
Robotic-assisted technologies have increasingly transformed spinal surgery over the past two decades by enhancing precision, minimizing operative risks, and improving clinical outcomes. This study conducted a comprehensive bibliometric analysis of global research on robotic applications in spinal surgery using a multi-tiered search strategy (Title–Abstract–Keywords, Abstract-only, and Title-only) in the Scopus database. Annual publication trends demonstrated consistent growth, with a pronounced rise after 2013, reflecting expanding clinical integration and research interest. Analysis of productivity revealed Tian, W. (55 publications), Theodore, N. (45), and Siewerdsen, J.H. (38) as the most prolific authors. Leading institutions included Harvard Medical School (110), Johns Hopkins University (108), and University of Toronto (103). The United States dominated country-level output with 2,826 publications, followed by China (1066) and Germany (657). Key funding agencies were the National Natural Science Foundation of China (361), NIH (240), and NSF (120), while the most productive journals included World Neurosurgery (254), Spine (185), and European Spine Journal (175). The top 100 most cited documents, published between 2001 and 2021 across 51 sources, comprised 80 original research articles and 20 reviews. These publications exhibited an average of 158.4 citations per document and a mean age of 12.4 years. Early publications (2001–2006) had high total citations but lower annual citation rates, whereas more recent studies (2017–2021) demonstrated increasing annual citation rates, peaking at 22 citations/year in 2020, reflecting growing research interest and impact in the field. Co-word analysis identified four dominant thematic clusters: (1) surgical precision and imaging-guided spinal interventions, (2) neurorehabilitation and functional recovery, (3) robotics in spinal oncology and radiotherapy, and (4) minimally invasive and perioperative safety-focused robotic procedures. Collectively, these themes demonstrate that high-impact research in robotic spinal surgery is driven by technological integration, enhanced surgical accuracy, and the pursuit of improved patient outcomes. Analysis of the top 25 papers reinforced the prominence of robotic guidance, comparative surgical studies, and rehabilitation-focused robotics. The findings clarify prevailing research directions and may assist in guiding the planning of future studies in this field.
Human papillomavirus (HPV) is a leading cause of cervical cancer globally. High-risk (HR) HPV types HPV16 and 18 are responsible for 70
