Metaphyseal trauma of the lower extremities in major orthopedic surgery as an independent risk factor for deep vein thrombosis.

PURPOSE: Major orthopedic surgeries of the lower extremities, which heavily injure the metaphyseal region, are strongly associated with the risk of developing deep vein thrombosis (DVT). This study aims to investigate the role of metaphyseal trauma as an independent risk factor for DVT. METHODS: Patients undergoing major orthopedic surgery of the hip and knee had their existing DVT risk factors recorded. Metaphyseal trauma was defined by the extent of bone injury during these surgeries. The samples were categorized into three surgery groups: total arthroplasty group (TA), hemiarthroplasty group (HA), and the open reduction internal fixation group (ORIF). Logistic regression test between significant existing risk factors and surgery groups determines the independent association between risk factors and DVT. RESULT: The study found a 24.8% incidence of asymptomatic DVT in patients undergoing major orthopedic surgeries, with the highest prevalence (37.2%) in TA, which had the largest extent of metaphyseal trauma and the least existing DVT risk factors. TA showed 6.2 OR and 95% CI (p = 0.036) compared to the other existing risk factor in relation to DVT incidence. CONCLUSION: Metaphyseal bone trauma in the hip and knee major orthopedic surgery is an independent risk factor for deep vein thrombosis.

Inductive detection of influence operations via graph learning.

Influence operations are large-scale efforts to manipulate public opinion. The rapid detection and disruption of these operations is critical for healthy public discourse. Emergent AI technologies may enable novel operations that evade detection and influence public discourse on social media with greater scale, reach, and specificity. New methods of detection with inductive learning capacity will be needed to identify novel operations before they indelibly alter public opinion and events. To this end, we develop an inductive learning framework that: (1) determines content- and graph-based indicators that are not specific to any operation; (2) uses graph learning to encode abstract signatures of coordinated manipulation; and (3) evaluates generalization capacity by training and testing models across operations originating from Russia, China, and Iran. We find that this framework enables strong cross-operation generalization while also revealing salient indicators-illustrating a generic approach which directly complements transductive methodologies, thereby enhancing detection coverage.