Microbiome-targeted therapies are redefining gastroenterology by delivering precision interventions that align with the body’s natural microbial ecosystem. This narrative review evaluates evidence for established approaches, probiotics, prebiotics, fecal microbiota transplantation (FMT), and postbiotics, and examines emerging innovations such as engineered probiotics, bacteriophage therapy, and metabolite-based interventions. Cure rates for recurrent Clostridium difficile infection in randomized trials range from 67% to 94%, depending on route and donor protocol, while multi-strain probiotics provide moderate benefits in inflammatory bowel disease. New modalities, including engineered bacteria and defined bacterial consortia, have progressed to Phase 3 trials, with several granted FDA breakthrough therapy designation. Approvals of Rebyota and Vowst mark a pivotal milestone, creating validated regulatory pathways for microbiome therapeutics. Despite progress, challenges remain in protocol standardisation, patient selection, cost-effectiveness, and clinical integration. Over 200 active trials and growing pharmaceutical investment signal a robust pipeline, with applications expanding to oncology, metabolic disorders, and immune modulation. Continued progress depends on validated biomarkers and personalized strategies guided by microbiome profiling. International regulatory harmonization will also be required to ensure safe and equitable adoption. The field is shifting toward working with, rather than against, the body’s microbial ecosystem, offering substantial potential for personalized gastrointestinal disease management.

Background: Postoperative intensive care unit (ICU) admission affects 15% to 20% of surgical patients and represents a major source of morbidity and health care costs. Current anesthetic dosing relies on empirical guidelines rather than individualized risk assessment. We developed a counterfactual dose-response model to identify optimal fentanyl-propofol combinations. Objective: This study aimed to develop and evaluate a stratified, causal machine learning framework using electronic health record data to identify optimal fentanyl-propofol dose combinations and predict postoperative ICU admission risk, enabling precision anesthesia and individualized clinical decision support. Methods: We analyzed perioperative electronic health records of 67,134 surgical procedures from UC Irvine Medical Center (2017‐2022). A hierarchical learning framework was used to estimate causal effects while controlling for confounding variables. A total of 6 dose-sensitive subgroups were identified through stratified analysis. The primary end point was postoperative ICU admission. Results: High-risk combinations (fentanyl >5 mcg/kg with propofol <1 mg/kg) increased ICU admissions’ absolute risk difference by 36% (absolute risk increase; 95% CI 0.351-0.509; P <.001). A total of 6 patient subgroups demonstrated distinct dose-response patterns, with populations considered vulnerable (high glucose, elevated creatinine) showing elevated risk even at standard doses. The optimal dose range for decision-making was determined to be 1.25 to 4.25 mg/kg for propofol and 3.5 to 4.0 mcg/kg for fentanyl. Conclusions: Fentanyl-propofol combinations exhibit complex, nonlinear dose-response relationships with ICU admission risk. High-dose combinations markedly increase risk through synergistic effects, while specific patient subgroups require enhanced monitoring even at standard doses. These findings support the development of individualized dosing algorithms and risk assessment tools that could inform future decision support tools aimed at reducing postoperative ICU use, although their predictive performance and clinical impact would require external validation.

Since federal legalisation in November 2016, Australia’s medical cannabis regulation has evolved into a complex framework that involves the Therapeutic Goods Administration, state health departments, and multiple professional oversight bodies. This narrative review examines the policy hurdles and coordination gaps that have emerged between 2016 and 2024, providing a longitudinal perspective on the ongoing debate. Evidence shows patient access expanded dramatically: the number of prescriptions jumped from 231 in 2017 to more than one million by early 2024, generating an estimated market value of AUD $445.6 million. Yet notable barriers remain, including monthly costs that average between $200 and $600, uneven availability in remote areas, and variable knowledge levels among general practitioners and specialists. Core obstacles, therefore, include the tangled federal-state regulatory maze, persistent equity problems for rural patients, challenges in embedding medical cannabis into standard clinical pathways, and a supply chain that still relies heavily on imported products. A comparative analysis with Canada’s opt-out insurance model, Germany’s pharmacist-led dispensing, and the Netherlands’ community-growth scheme highlights Australia’s relatively restrictive approach, particularly in terms of reimbursement and comprehensive provider training. Policy options thus centre on extending Pharmaceutical Benefits Scheme (PBS) subsidies, building domestic cultivation and manufacturing capacity, mandating uniform educational modules for prescribers, and streamlining inter-agency oversight. This review aims to inform Australian reforms while also offering transferable lessons for other jurisdictions contemplating or revising therapeutic cannabis programmes.

Universal healthcare systems are under mounting pressure from demographic change, technological advancement, and fiscal constraints. This narrative review compares two high-performing health economic models—the United Kingdom’s National Health Service (NHS) and Australia’s Medicare, each grounded in distinct philosophical and structural foundations. This review analyses their respective funding architectures, cost-effectiveness, and sustainability challenges. The NHS operates a taxation-funded, vertically integrated system with a £185.8 billion budget, while Medicare Australia combines a 2% levy with widespread private insurance, covering 46% of the population for inpatient care. Despite similar demographic pressures, Australia ranks higher (1st vs. 3rd) in the 2024 Commonwealth Fund assessment. The NHS benefits from monopsonistic cost control but faces capacity constraints; Medicare’s hybrid design offers flexibility yet raises equity concerns. Both systems have pursued major reforms since 2015, including the NHS’s Integrated Care Systems and Australia’s expanded bulk-billing incentives. The COVID-19 pandemic accelerated digital health adoption, with telehealth consultations surging to over 30% of total usage at their peak. Long-term sustainability in both systems hinges on addressing aging populations, chronic workforce shortages, and the need to redesign service delivery beyond financial solutions.

Background: The Tabular Prior-data Fitted Network (TabPFN) is a recently introduced Transformer-based foundation model designed specifically for structured tabular data. TabPFN enables task inference without the need for hyperparameter tuning or extensive data preprocessing. Despite its disruptive potential, the application of TabPFN in surgical data science remains unexplored. In this study, we evaluate the performance of TabPFN across six surgical classification tasks. Objective: To assess TabPFN’s performance against benchmark machine learning models in surgical classification tasks and identify optimal application scenarios based on sample size and outcome incidence characteristics. Methods: In this study, perioperative data from two independent medical centres were utilized, comprising a large-scale cohort (n=67,134) and a medium-scale cohort (n=6,888). Six clinically relevant classification tasks were developed. The performance of TabPFN was systematically compared to benchmark models including XGBoost, Random Forest, Support vector machine, Logistic regression, and Decision tree, using area under the receiver operating characteristic curve, accuracy, precision, recall, F1-score, and calibration. Results: In tasks with large-sample sizes (n > 3,000) and higher outcome incidence (>40%), TabPFN achieved the highest recall and F1-score among all models. For tasks with low outcome incidence ( 3,000), but its calibration performance declined noticeably in low outcome incidence (<20%). Conclusions: TabPFN represents a promising methodological approach for tabular modelling in surgical data science. It also shows considerable promise in tasks with sample sizes exceeding 10,000. However, it is not yet capable of fully replacing established benchmark models. The application scenarios of TabPFN should be selected based on key task-specific characteristics. Further targeted training is necessary in future.