Among the papers published in the December issue of Neurospine, the featured articles selected by editors are as follows. "Bone Remodeling and Modeling: Cellular Targets for Antiresorptive and Anabolic Treatments, Including Approaches Through the Parathyroid Hormone (PTH)/PTH-Related Protein Pathway" by Martin and Seeman. 1 Bone remodeling, balancing formation and resorption, occurs across the skeleton. Antiresorptive drugs have been key in treating bone loss and fractures. Recent treatments, such as parathyroid hormone (PTH) and its analog, PTH-related protein, show promise in restoring bone volume and preventing fractures. Additionally, therapies targeting the Wnt signaling pathway, particularly through the inhibition of sclerostin, represent a significant development. Despite these anabolic advancements, the ongoing need for antiresorptive drugs remains critical to maintain restored bone. "Utilization of Vertebroplasty/Kyphoplasty in the Management of Compression Fractures: National Trends and Predictors of Vertebroplasty/Kyphoplasty" by O'Neill et al. 2This study analyzed the use of kyphoplasty/vertebroplasty in managing compression fractures, particularly amid the increasing elderly and osteoporosis-affected population. Examining data from 91 million patients, it found that only 9.2% of patients with compression fractures received these procedures as initial treatment. Patients undergoing kyphoplasty/ vertebroplasty were typically older, female, obese, smokers, and had higher comorbidity scores. The study identified female sex, smoking status, and obesity as strong predictors for receiving these treatments. Despite the prevalence of vertebral compression fractures, most are managed nonoperatively, and the annual rate of kyphoplasty/vertebroplasty remained stable between 8% and 11%. Lee et al. 3 This study aimed to create a personalized risk calculator for proximal junctional kyphosis (PJK) after adult spinal deformity (ASD) surgery. Analyzing data from 201 patients, it identified postoperative proximal junctional angle, body mass index, and deformity type as key predictors for PJK. A random forest machine learning model, chosen for its high accuracy (83%) and good predictive performance, was used to develop an online risk calculator.
"Development and Validation of an Online Calculator to Predict Proximal Junctional Kyphosis After Adult Spinal Deformity Surgery Using Machine Learning" by