Systematic review of 99 extremity bone malignancy survival prediction models.

Background: Various prediction models have been developed for extremity metastasis and sarcoma. This systematic review aims to evaluate extremity metastasis and sarcoma models using the utility prediction model (UPM) evaluation framework.

Methods: We followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and systematically searched PubMed, Embase, and Cochrane to identify articles presenting original prediction models with 1-year survival outcome for extremity metastasis and 5-year survival outcome for sarcoma.

External validation of the SORG machine learning for 90-day and 1-year mortality in patients suffering from extremity metastatic disease in an European cohort of 174 patients.

Accurate survival prediction of patients with long-bone metastases is challenging, but important for optimizing treatment. The Skeletal Oncology Research Group (SORG) machine learning algorithm (MLA) has been previously developed and internally validated to predict 90-day and 1-year survival. External validation showed promise in the United States and Taiwan.

Long Dosing Intervals of Parenteral Antiosteoporosis Medications and the Decrease in Societal Fracture Risk: An 11-Year Nationwide Population-Based Cohort Study.

Objective: To evaluate the relationship between different dosing intervals of antiosteoporosis medications (AOMs) and the subsequent fracture risk among patients with newly initiated AOM therapies.

Patients And Methods: In a nationwide population-based cohort study based on Taiwan's National Health Insurance Research Database, osteoporosis patients with 50 years of age or older who newly initiated AOM from January 1, 2008, to December 31, 2018 (n=336,229) were included. We categorized AOMs into short dosing intervals (oral AOMs) or long dosing intervals (parenteral AOMs).

Temporal validation of the SORG 90-Day and 1-Year machine learning algorithms for survival of patients with spinal metastatic disease.

Purpose: The SORG-MLA was developed to predict 90-day and 1-year postoperative survival in patients with spinal metastatic disease who underwent surgery between 2000 and 2016. Due to the constant changes in treatment methods, it is essential to perform temporal validation with a recent patient population. Therefore, the purpose of this study was to validate the Skeletal Oncology Research Group machine learning algorithms (SORG-MLA) using a contemporary patient cohort.

Comparative Analysis of Surgical Outcomes in Separation Surgery vs. Anterior Reconstruction for Metastatic Epidural Spinal Cord Compression.

Study Design: Retrospectively matched case-control study.

Objective: To compare intraoperative and postoperative outcomes between separation surgery and corpectomy with anterior reconstruction for patients with metastatic epidural spinal cord compression (MESCC).

Summary Of Background Data: The primary goal of surgery for MESCC is to preserve and improve neurological function.