Performance of a Large Language Model in the Generation of Clinical Guidelines for Antibiotic Prophylaxis in Spine Surgery.

Objective: Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT's 2 models (GPT-3.5 and GPT-4.

Can generative artificial intelligence pass the orthopaedic board examination?

Background: Resident training programs in the US use the Orthopaedic In-Training Examination (OITE) developed by the American Academy of Orthopaedic Surgeons (AAOS) to assess the current knowledge of their residents and to identify the residents at risk of failing the Amerian Board of Orthopaedic Surgery (ABOS) examination. Optimal strategies for OITE preparation are constantly being explored. There may be a role for Large Language Models (LLMs) in orthopaedic resident education.

Performance of ChatGPT on NASS Clinical Guidelines for the Diagnosis and Treatment of Low Back Pain: A Comparison Study.

Study Design: Comparative analysis.

Objective: To evaluate Chat Generative Pre-trained Transformer (ChatGPT's) ability to predict appropriate clinical recommendations based on the most recent clinical guidelines for the diagnosis and treatment of low back pain.

Background: Low back pain is a very common and often debilitating condition that affects many people globally.

Impact of tobacco usage on readmission and complication rates following shoulder replacement surgery: A study of 164,527 patients.

Background: Tobacco carcinogens have adverse effects on bone health and are associated with inferior outcomes following orthopedic procedures. The purpose of this study was to assess the impact tobacco use has on readmission and complication rates following shoulder arthroplasty.

Methods: The 2016-2018 National Readmissions Database was queried to identify patients who underwent anatomical, reverse, and hemi-shoulder arthroplasty.

Association Between Age-stratified Cohorts and Perioperative Complications and 30-day and 90-day Readmission in Patients Undergoing Single-level Anterior Cervical Discectomy and Fusion.

Study Design: Retrospective analysis.

Objective: To assess perioperative complication rates and readmission rates after ACDF in a patient population of advanced age.

Summary Of Background Data: Readmission rates after ACDF are important markers of surgical quality and, with recent shifts in reimbursement schedules, they are rapidly gaining weight in the determination of surgeon and hospital reimbursement.

Thromboembolic prophylaxis in spine surgery: an analysis of ChatGPT recommendations.

Background Context: Venous thromboembolism is a negative outcome of elective spine surgery. However, the use of thromboembolic chemoprophylaxis in this patient population is controversial due to the possible increased risk of epidural hematoma. ChatGPT is an artificial intelligence model which may be able to generate recommendations for thromboembolic prophylaxis in spine surgery.

Diabetes increases risk for readmission and infection after shoulder arthroplasty: A national readmissions study of 113,713 patients.

Background: The recent increasing popularity of shoulder arthroplasty has been paralleled by a rise in prevalence of diabetes in the United States. We aimed to evaluate the impact of diabetes status on readmission and short-term complications among patients undergoing shoulder arthroplasty.

Methods: We analyzed the Healthcare Cost and Utilization Project National Readmissions Database (NRD) between the years 2016-2018.

Robust prediction of nonhome discharge following elective anterior cervical discectomy and fusion using explainable machine learning.

Purpose: Predict nonhome discharge (NHD) following elective anterior cervical discectomy and fusion (ACDF) using an explainable machine learning model.

Methods: 2227 patients undergoing elective ACDF from 2008 to 2019 were identified from a single institutional database. A machine learning model was trained on preoperative variables, including demographics, comorbidity indices, and levels fused.

Building better pediatric surgeons: A sentiment analysis of online physician review websites.

Purpose: Physician review websites are a heavily utilized patient tool for finding, rating, and reviewing surgeons. Natural language processing such as sentiment analysis provides a comprehensive approach to better understand the nuances of patient perception. This study utilizes sentiment analysis to examine how specific patient sentiments correspond to positive and negative experiences in online reviews of pediatric orthopedic surgeons.

A national analysis of the effect alcohol use disorder has on short-term complications and readmissions following total shoulder arthroplasty.

Background: Alcohol use disorder has been associated with broad health consequences that may interfere with healing after total shoulder arthroplasty. The aim of this study was to explore the impact of alcohol use disorder on readmissions and complications following total shoulder arthroplasty.

Methods: We used data from the Healthcare Cost and Utilization Project National Readmissions Database (NRD) from 2016 to 2018.

Trends in the Charges and Utilization of Computer-Assisted Navigation in Cervical and Thoracolumbar Spinal Surgery.

Study Design: Retrospective national database study.

Purpose: This study is conducted to assess the trends in the charges and usage of computer-assisted navigation in cervical and thoracolumbar spinal surgery.

Overview Of Literature: This study is the first of its kind to use a nationwide dataset to analyze trends of computer-assisted navigation in spinal surgery over a recent time period in terms of use in the field as well as the cost of the technology.

Understanding Artificial Intelligence and Predictive Analytics: A Clinically Focused Review of Machine Learning Techniques.

»: Machine learning and artificial intelligence have seen tremendous growth in recent years and have been applied in numerous studies in the field of orthopaedics.

»: Machine learning will soon become critical in the day-to-day operations of orthopaedic practice; therefore, it is imperative that providers become accustomed to and familiar with not only the terminology but also the fundamental techniques behind the technology.

»: A foundation of knowledge regarding machine learning is critical for physicians so they can begin to understand the details in the algorithms that are being developed, which provide improved accuracy compared with clinicians, decreased time required, and a heightened ability to triage patients.

The Impact of Computer-Assisted Navigation on Charges and Readmission in Patients Undergoing Posterior Cervical Fusion Surgery.

Study Design: Retrospective cohort study of 2016 Healthcare Cost and Utilization Project Nationwide Readmissions Database (NRD).

Objective: The aim was to evaluate cost and outcomes associated with navigation use on posterior cervical fusion (PCF) surgery patients.

Summary Of Background Data: Computer-assisted navigation systems demonstrate comparable outcomes with hardware placement and procedural speed compared with traditional techniques.

How Are Patients Reviewing Spine Surgeons Online? A Sentiment Analysis of Physician Review Website Written Comments.

Study Design: A Sentiment Analysis of online reviews of spine surgeons.

Objectives: Physician review websites have significant impact on a patient's provider selection. Written reviews are subjective, but sentiment analysis through machine learning can quantitatively analyze these reviews.

Using Sentiment Analysis to Understand What Patients Are Saying About Hand Surgeons Online.

Background: Physician review websites have influence on a patient's selection of a provider. Written reviews are subjective and difficult to quantitatively analyze. Sentiment analysis of writing can quantitatively assess surgeon reviews to provide actionable feedback for surgeons to improve practice.

Readmission and Associated Factors in Surgical Versus Non-Surgical Management of Spinal Epidural Abscess: A Nationwide Readmissions Database Analysis.

Study Design: Retrospective cohort study.

Objectives: Spinal epidural abscess (SEA) is a rare but potentially life-threatening infection treated with antimicrobials and, in most cases, immediate surgical decompression. Previous studies comparing medical and surgical management of SEA are low powered and limited to a single institution.

Scoliosis surgery in social media: a natural language processing approach to analyzing the online patient perspective.

Purpose: The purpose of this study is to analyze posts shared on Instagram, Twitter, and Reddit referencing scoliosis surgery to evaluate content, tone, and perspective.

Methods: Public posts from Instagram, Twitter, and Reddit were parsed in 2020-2021 and selected based on inclusion of the words 'scoliosis surgery' or '#scoliosissurgery. 100 Reddit posts, 5022 Instagram posts, and 1414 tweets were included in analysis.

What are patients saying about you online? A sentiment analysis of online written reviews on Scoliosis Research Society surgeons.

Purpose: Physician review websites have significant influence on a patient's selection of a provider, but written reviews are subjective. Sentiment analysis of writing through artificial intelligence can quantify surgeon reviews to provide actionable feedback. The objective of this study is to quantitatively analyze the written reviews of members of the Scoliosis Research Society (SRS) through sentiment analysis.