Analysis of longitudinal patterns of child maltreatment reports in the United States.

Background: Child maltreatment is a continuous and prevalent issue, and victims of maltreatment often suffer adverse effects well into adulthood. Since child maltreatment rates tend to be clustered geographically and temporally, intervention programs are best implemented at a local level, targeting local risk factors for sustained and effective reduction over time.

Objective: Quantifying geographic variation in child maltreatment rate trajectories can help states identify local risk factors to guide program development and resource allocation.

Alterations in large-scale resting-state network nodes following transcranial focused ultrasound of deep brain structures.

Background: Low-intensity transcranial focused ultrasound (tFUS) is a brain stimulation approach that holds promise for the treatment of brain-based disorders. Studies in humans have shown that tFUS can successfully modulate perfusion in focal sonication targets, including the amygdala; however, limited research has explored how tFUS impacts large-scale neural networks.

Objective: The aim of the current study was to address this gap and examine changes in resting-state connectivity between large-scale network nodes using a randomized, double-blind, within-subjects crossover study design.

Axillary Surgery in Breast Cancer - Primary Results of the INSEMA Trial.

Background: Whether surgical axillary staging as part of breast-conserving therapy can be omitted without compromising survival has remained unclear.

Methods: In this prospective, randomized, noninferiority trial, we investigated the omission of axillary surgery as compared with sentinel-lymph-node biopsy in patients with clinically node-negative invasive breast cancer staged as T1 or T2 (tumor size, ≤5 cm) who were scheduled to undergo breast-conserving surgery. We report here the per-protocol analysis of invasive disease-free survival (the primary efficacy outcome).

Artificial Intelligence-Driven Patient Selection for Preoperative Portal Vein Embolization for Patients with Colorectal Cancer Liver Metastases.

Purpose: To develop a machine learning algorithm to improve hepatic resection selection for patients with metastatic colorectal cancer (CRC) by predicting post-portal vein embolization (PVE) outcomes.

Materials And Methods: This multicenter retrospective study (2000-2020) included 200 consecutive patients with CRC liver metastases planned for PVE before surgery. Data on radiomic features and laboratory values were collected.