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TIVA confers no recovery advantage over inhalational anesthesia; supports either approach in older adults undergoing major noncardiac surgery.

Anesthesiologist, Unknown
First of all, congratulations both to Joyce, Shaman, and the people group, you know, and the U.K. for actually executing this trial and recruiting. So it's a fabulous feat. I think as you said, it's absolutely a positive study... in the sense that it's reassuring that the techniques that are available to us as a community... it's absolutely reassuring that whatever anesthetic choice we have available to us, patients will do well.
May support phenotype-guided sacubitril/valsartan in HFpEF; leaves open prospective validation before practice change.

Meta-analysis reveals coronary CT markers predict perioperative major adverse cardiac events in noncardiac surgery, indicating strong value for preoperative cardiovascular risk stratification.

National AF hospitalization trends reported; leaves open causal factors and targeted interventions to reduce readmissions.

INTRODUCTION: Cardiovascular (CV) adverse events are increasingly recognized in patients with cancer. Previous reviews of AI/ML have focused on single cancer types, imaging-based data, and lacked evaluation of methodological rigor. This systematic review synthesized AI/ML models developed to predict CV adverse events from patient-level clinical data across diverse cancer populations. METHODS: This review followed the PRISMA 2020 guidelines. PubMed and Web of Science were searched through 26 October 2025. Study characteristics, model development, and handling of features and missing data were extracted. Study quality was assessed using the IJMEDI checklist. RESULTS: Of 32 included studies, 18 compared multiple algorithms and 14 used a single algorithm. Random forest and XGBoost were the most common methods (n = 17, respectively), and XGBoost was most often the best-performing model in multi-algorithm studies, although substantial study heterogeneity precludes concluding general algorithmic superiority. Common limitations were unreported missing data handling (n = 17), limited external validation (n = 8), and rare calibration assessment (n = 4). Most studies were rated medium quality (n = 28). CONCLUSIONS: AI/ML models show promise for predicting CV adverse events in patients with cancer; however, clinical applicability is constrained by insufficient preprocessing transparency, limited external validation, and inadequate calibration reporting.

TS approach linked to longer operative times and higher pacemaker risk than LA in MVS; extends pooled evidence favoring LA to minimize complications.

Supports ARNI consideration in dialysis HFrEF; extends observational evidence but leaves open need for RCTs.

May support either technique on cost-effectiveness grounds; leaves open confirmation in larger trials.

May support 1.8–4 g/day omega-3 for arterial stiffness reduction; extends meta-analyses by clarifying dose heterogeneity.

Supports COM-B-guided nursing programs in HF self-care; extends theory-driven intervention evidence for implementation trials.
