Assessing the ability of large language models to simplify lumbar spine imaging reports into patient-facing text: a pilot study of GPT-4.

Objective: To assess the ability of large language models (LLMs) to accurately simplify lumbar spine magnetic resonance imaging (MRI) reports.

Materials And Methods: Patients who underwent lumbar decompression and/or fusion surgery in 2022 at one tertiary academic medical center were queried using appropriate CPT codes. We then identified all patients with a preoperative ICD diagnosis of lumbar spondylolisthesis and extracted the latest preoperative spine MRI radiology report text.

Early Emergency Department Visits are a Predictor of Increased Health Care Utilization Within 1 Year After Elective Spine Surgery for Degenerative Pathologies.

Study Design: Retrospective cohort study.

Objectives: The purpose of this study is to determine the incidence of and risk factors for emergency department (ED) utilization following elective spine surgery and to quantify health care utilization in this subpopulation of patients.

Summary Of Background Data: The Centers for Medicare and Medicaid Services (CMS) imposes financial penalties for unplanned 30-day hospital readmissions following the surgery.

Upper-Level Instrumentation at C2 Versus C3 Does Not Influence Radiographic or Clinical Outcomes After Posterior Cervical Fusion.

Study Design: Retrospective cohort study.

Objective: To assess clinical and radiographic outcomes after posterior cervical decompression and fusion (PCDF) with upper instrumented vertebra (UIV) at C2 versus C3.

Summary Of Background Data: PCDF is a common procedure for treatment of multilevel cervical spondylotic myelopathy.

Paraspinal sarcopenia independently predicts patient-reported outcomes following open but not minimally invasive single-level lumbar decompression.

Background Context: Sarcopenia is a known risk factor for inferior outcomes following spine surgery. However, few studies have investigated the optimal method or cutoff to quantify paraspinal sarcopenia on MRI for outcome prediction. Furthermore, no studies have compared the impact of paraspinal sarcopenia on outcomes after minimally invasive versus open decompression surgery.

Identifying patients at risk of increased health utilization following lumbar spine surgery.

Background: Adequate preoperative identification of patients at risk of significant healthcare utilization after surgery could help guide preoperative decision-making as well as postoperative patient management. While several studies have proposed mechanisms and risk factors for healthcare utilization, no studies have developed a prognostic machine learning model to quantify and functionalize predictions.

Methods: A cohort of lumbar fusion and lumbar decompression surgeries was queried from a tertiary academic medical center from 2002 to 2022.

Identifying Predictors of Failed Back Surgery Syndrome Following Lumbar Spine Surgery: A Machine Learning Approach.

Study Design: Retrospective cohort study from a tertiary academic medical center.

Objective: To build a prognostic machine learning model to predict 1-year FBSS incidence following lumbar spine surgery.

Summary Of Background Data: A minority of patients who undergo degenerative lumbar spine surgery will have persistent postoperative pain, characterized as "Failed Back Surgery Syndrome" (FBSS).

The Influence of Paraspinal Sarcopenia on Patient-Reported Outcomes Following Lumbar Fusion.

Study Design: Retrospective cohort study.

Objective: To analyze the ability of various quantitative, MRI-based measures of spine-specific sarcopenia to predict patient-reported outcomes following lumbar fusion surgery.

Summary Of Background Data: Sarcopenia is an established risk factor for poor outcomes and complications in spine surgery.

A Regional Cost and Outcomes Analysis of Outpatient Single Level Anterior Cervical Discectomy and Fusion (ACDF) in the United States: 2016-2022.

Study Design: Retrospective healthcare claims database study.

Objective: To assess variations in costs and outcomes for single ACDFs in the outpatient setting across census bureau divisions.

Summary Of Background Data: The proportion of ACDFs performed in the outpatient setting is growing rapidly in the United States.

Does PROMIS Identify Recall Accuracy and Bias in Elective Spine Surgery Patients?

Study Design: Prospective study (level II evidence).

Objective: The objective of this study was to assess recall accuracy and bias through PROMIS-PF (physical function) and PI (pain interference) scores in patients undergoing cervical or lumbar spine surgery.

Summary Of Background Data: Patient-reported outcomes (PROs) have improved quantitative data availability; however, the interpretation of results may remain susceptible to confounding factors including recall accuracy.

Predictive Value of Social Determinants of Health on 90-Day Readmission and Health Utilization Following ACDF: A Comparative Analysis of XGBoost, Random Forest, Elastic-Net, SVR, and Deep Learning.

Study DesignRetrospective cohort.ObjectiveDespite numerous studies highlighting patient comorbidities and surgical factors in postoperative success, the role of social determinants of health (SDH) in anterior cervical discectomy and fusion (ACDF) outcomes remains unexplored. This study evaluates the predictive impact of SDH on 90-day readmission and health utilization (HU) in ACDF patients using machine learning (ML).

The prevalence of congenital cervical stenosis differs based on race.

Background: Congenital cervical stenosis (CCS) is a rare condition involving a narrowed spinal canal due to developmental anomalies. CCS heightens the risk of neurologic deficits and acute spinal cord injury posttrauma, influencing return-to-play decisions for contact athletes. Additionally, CCS patients are prone to cervical myelopathy as degenerative changes progress with age.

A meta-analysis of the American college of surgeons risk calculator's predictive accuracy among different surgical sub-specialties.

Background: The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) provides risk estimates of postoperative complications. While several studies have examined the accuracy of the ACS-Surgical Risk Calculator (SRC) within a single specialty, the respective conclusions are limited by sample size. We sought to conduct a meta-analysis to determine the accuracy of the ACS-SRC among various surgical specialties.

Quantifying Patient Expectations for Total Knee Arthroplasty: Expectations for Improvement Are Greater Than Minimal Clinically Important Difference.

Background: Achieving a minimal clinically important difference (MCID) in patient-reported outcomes following total knee arthroplasty (TKA) is common, yet up to 20% patient dissatisfaction persists. Unmet expectations may explain post-TKA dissatisfaction. No prior studies have quantified patient expectations using the same patient-reported outcome metric as used for MCID to allow direct comparison.

A Novel Method for Tracking Neck Motions Using a Skin-Conformable Wireless Accelerometer: A Pilot Study.

Introduction: Cervical spine disease is a leading cause of pain and disability. Degenerative conditions of the spine can result in neurologic compression of the cervical spinal cord or nerve roots and may be surgically treated with an anterior cervical discectomy and fusion (ACDF) in up to 137,000 people per year in the United States. A common sequelae of ACDF is reduced cervical range of motion (CROM) with patient-based complaints of stiffness and neck pain.

Does Operative Level Impact Dysphagia Severity After Anterior Cervical Discectomy and Fusion?: A Multicenter Prospective Analysis.

Study Design: Prospective multicenter cohort study.

Objective: To explore the association between operative level and postoperative dysphagia after anterior cervical discectomy and fusion (ACDF).

Background: Dysphagia is common after ACDF and has several risk factors, including soft tissue edema.

AOA Critical Issues Symposium: Shaping the Impact of Artificial Intelligence within Orthopaedic Surgery.

Artificial intelligence (AI) is a broad term that is widely used but inconsistently understood. It refers to the ability of any machine to exhibit human-like intelligence by making decisions, solving problems, or learning from experience. With its ability to rapidly process large amounts of information, AI has already transformed many industries such as entertainment, transportation, and communications through consumer-facing products and business-to-business applications.

An Investment in Knowledge: Personal Finance Education for Medical Students.

The financial burden of pursuing a medical education continues to grow, with the average medical student now owing over $240,000 in total student loan debt by the time they graduate. This burden peaks at a time when trainees are making some of the most consequential decisions of their careers. Additionally, many students are simultaneously making important financial decisions related to personal aspirations, all before a drastic change in earning potential once they begin practicing as attending physicians.

Representativeness of the American Spine Registry: a comparison of patient characteristics with the National Inpatient Sample.

Objective: The American Spine Registry (ASR) is a collaborative effort between the American Academy of Orthopaedic Surgeons and the American Association of Neurological Surgeons. The goal of this study was to evaluate how representative the ASR is of the national practice with spinal procedures, as recorded in the National Inpatient Sample (NIS).

Methods: The authors queried the NIS and the ASR for cervical and lumbar arthrodesis cases performed during 2017-2019.

Patient and Surgeon Perceptions Regarding Microdiscectomy Surgery: A Cross-Sectional Analysis of Social Media Posts.

Background: Social media offers a powerful and expanding platform for sharing the patient experience with a large audience through an unsolicited perspective. The content may influence future perceptions around surgical care.

Objective: To analyze publicly available content on a major social media outlet related to microdiscectomy surgery based on perspective, location, timing, content, tone, and patient satisfaction.

Using Machine Learning and Deep Learning Algorithms to Predict Postoperative Outcomes Following Anterior Cervical Discectomy and Fusion.

Study Design: A retrospective cohort study from a multisite academic medical center.

Objective: To construct, evaluate, and interpret a series of machine learning models to predict outcomes related to inpatient health care resource utilization for patients undergoing anterior cervical discectomy and fusion (ACDF).

Summary Of Background Data: Reducing postoperative health care utilization is an important goal for improving the delivery of surgical care and serves as a metric for quality assessment.

The Effect of Diabetes and Metabolic Syndrome on Spine Surgery Outcomes.

Purpose Of Review: Diabetes and metabolic syndrome are highly prevalent in patients undergoing spine surgery. This review aims to capture both the findings of recently published literature investigating the effects of diabetes and metabolic syndrome on spine surgery outcomes and the current best practices in patient management.

Recent Findings: Diabetes and metabolic syndrome both contribute to worse outcomes in patients undergoing spine surgery.