Disparities in Operative Fixation for Acromioclavicular Joint Injuries: An Analysis of Insurance Status.

Background: Acromioclavicular (AC) joint injury management has historically been guided by the severity and grade of the injury. However, recent debates have emerged regarding the role of surgical intervention for these injuries. Insurance-based disparities in surgical treatment have been well-documented across various orthopedic conditions.

Multiplexed mosaic tumor models reveal natural phenotypic variations in drug response within and between populations.

Many agents that show promise in preclinical cancer models lack efficacy in patients due to patient heterogeneity that is not captured in traditional assays. To address this problem, we have developed GENEVA, a platform that measures the molecular and phenotypic consequences of drug perturbations within diverse populations of cancer cells at single-cell resolution, both and . Here, we apply GENEVA to study the KRAS G12C inhibitors, recapitulating known properties of these drugs and uncovering a previously unknown role for mitochondrial activation in cell death induced by KRAS inhibition.

Utility of ChatGPT as a preparation tool for the Orthopaedic In-Training Examination.

Purpose: Chat Generative Pre-Trained Transformer (ChatGPT) may have implications as a novel educational resource. There are differences in opinion on the best resource for the Orthopaedic In-Training Exam (OITE) as information changes from year to year. This study assesses ChatGPT's performance on the OITE for use as a potential study resource for residents.

BAE-ViT: An Efficient Multimodal Vision Transformer for Bone Age Estimation.

This research introduces BAE-ViT, a specialized vision transformer model developed for bone age estimation (BAE). This model is designed to efficiently merge image and sex data, a capability not present in traditional convolutional neural networks (CNNs). BAE-ViT employs a novel data fusion method to facilitate detailed interactions between visual and non-visual data by tokenizing non-visual information and concatenating all tokens (visual or non-visual) as the input to the model.