HIV self-testing knowledge, attitudes, and acceptance in New York City.

BackgroundHIV testing is a critical strategy for prevention. During the COVID-19 pandemic, many community-based organizations (CBOs) offering free HIV testing were unable to provide in-person services. Innovations like free HIV Self-Testing (HIVST), provided a solution and can be done outside clinical settings, help address barriers such as cost, availability, and stigma.

Role of preoperative Hounsfield units in predicting subsidence after anterior cervical discectomy and fusion in the United States: a retrospective analysis including osteopenia diagnosis.

Study Design: A retrospective cohort study.

Purpose: This study aimed to investigate the relationship among osteopenia, bone density, and subsidence following anterior cervical discectomy and fusion (ACDF).

Overview Of Literature: Subsidence following ACDF procedures can lead to worse clinical outcomes.

Development of a novel machine learning model to automate vertebral column segmentation utilizing biplanar full-body imaging.

Background Context: Degenerative scoliosis (DS) is a common spinal disorder among adults, characterized by lateral curvature of the spine. Recent advancements in biplanar full-body imaging, a low-dose and weight-bearing X-ray modality, facilitate safer and longitudinal imaging of DS patients. Quantifying spinal curvature serves as a valuable metric for assessing DS severity and informing surgical planning.

Can generative artificial intelligence provide accurate medical advice?: a case of ChatGPT versus Congress of Neurological Surgeons management of acute cervical spine and spinal cord injuries clinical guidelines.

Study Design: An experimental study.

Purpose: To explore the concordance of ChatGPT responses with established national guidelines for the management of cervical spine and spinal cord injuries.

Overview Of Literature: ChatGPT-4.

Management of lower extremity traumas: Comparing appropriate use criteria ChatGPT recommendations.

Background: High-energy lower extremity injury presents with difficult clinical decisions because successful limb salvage is the best scenario for complex traumas, but early amputation may be necessary to limit complications. Artificial Intelligence is a tool rising in popularity to help make clinical judgements.

Purpose/questions: The aim of this study is to determine whether ChatGPT-4 can produce accurate recommendations for limb salvage or amputation given various patient scenarios.

Earlier Initiation of Supervised Physical Rehabilitation Is Not Associated With Decreased Arthrofibrosis After Isolated Arthroscopic Anterior Cruciate Ligament Reconstruction.

Purpose: To identify whether the timing of supervised physical rehabilitation initiation after isolated arthroscopic anterior cruciate ligament reconstruction (ACLR) is associated with (1) diagnosis of arthrofibrosis and (2) surgical intervention for arthrofibrosis within 12 months after surgery.

Methods: Outpatient isolated arthroscopic ACLR procedures in 2017-2020 were identified from the Merative MarketScan database. The cohort was limited to patients aged 18 to 64 years who initiated supervised physical rehabilitation at 0 to 30 days postoperatively and had continuous enrollment in the database for 12 months before and after surgery.

Limited Use of Supervised Physical Rehabilitation Beyond 3 Months After Arthroscopic Anterior Cruciate Ligament Reconstruction With Greater Use in Female and Younger Patients.

Purpose: To characterize the initiation and use of supervised physical rehabilitation after arthroscopic anterior cruciate ligament reconstruction (ACLR), including overall duration of rehabilitation and number of rehabilitation visits, and to describe demographic and clinical predictors of rehabilitation initiation and use characteristics.

Methods: Patients aged 14 to 64 years in the United States who underwent ACLR from 2017 to 2020 were identified using the Merative MarketScan Database. For patients initiating rehabilitation within 45 days postoperatively, the overall duration and number of visits within 1 year after surgery were determined.

Explainable Machine Learning Approach to Prediction of Prolonged Intensive Care Unit Stay in Adult Spinal Deformity Patients: Machine Learning Outperforms Logistic Regression.

Study DesignRetrospective cohort study.ObjectivesProlonged ICU stay is a driver of higher costs and inferior outcomes in Adult Spinal Deformity (ASD) patients. Machine learning (ML) models have recently been seen as a viable method of predicting pre-operative risk but are often 'black boxes' that do not fully explain the decision-making process.

Can Large Language Models (LLMs) Predict the Appropriate Treatment of Acute Hip Fractures in Older Adults? Comparing Appropriate Use Criteria With Recommendations From ChatGPT.

Background: Acute hip fractures are a public health problem affecting primarily older adults. Chat Generative Pretrained Transformer may be useful in providing appropriate clinical recommendations for beneficial treatment.

Objective: To evaluate the accuracy of Chat Generative Pretrained Transformer (ChatGPT)-4.

Ambulatory Surgery Centers Reduce Patient Out-of-Pocket Expenditures for Isolated Arthroscopic Rotator Cuff Repair, but Patient Out-of-Pocket Expenditures Are Increasing at a Faster Rate Than Total Healthcare Utilization Reimbursement From Payers.

Purpose: To categorize and trend annual out-of-pocket expenditures for arthroscopic rotator cuff repair (RCR) patients relative to total healthcare utilization (THU) reimbursement and compare drivers of patient out-of-pocket expenditures (POPE) in a granular fashion via analyses by insurance type and surgical setting.

Methods: Patients who underwent outpatient arthroscopic RCR in the United States from 2013 to 2018 were identified from the IBM MarketScan Database. Primary outcome variables were total POPE and THU reimbursement, which were calculated for all claims in the 9-month perioperative period.

Anticoagulation Versus Antiplatelets in Spontaneous Cervical Artery Dissection: A Systematic Review and Meta-Analysis.

Background: It is uncertain whether antiplatelets or anticoagulants are more effective in preventing early recurrent stroke in patients with cervical artery dissection. Following the publication of the observational Antithrombotic for STOP-CAD (Stroke Prevention in Cervical Artery Dissection) study, which has more than doubled available data, we performed an updated systematic review and meta-analysis comparing antiplatelets versus anticoagulation in cervical artery dissection.

Methods: The systematic review was registered in PROSPERO (CRD42023468063).

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.

ChatGPT versus NASS clinical guidelines for degenerative spondylolisthesis: a comparative analysis.

Background Context: Clinical guidelines, developed in concordance with the literature, are often used to guide surgeons' clinical decision making. Recent advancements of large language models and artificial intelligence (AI) in the medical field come with exciting potential. OpenAI's generative AI model, known as ChatGPT, can quickly synthesize information and generate responses grounded in medical literature, which may prove to be a useful tool in clinical decision-making for spine care.

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.

Transcarotid Arterial Revascularization of Symptomatic Internal Carotid Artery Disease: A Systematic Review and Study-Level Meta-Analysis.

Background: Transcarotid artery revascularization (TCAR) is an interventional therapy for symptomatic internal carotid artery disease. Currently, the utilization of TCAR is contentious due to limited evidence. In this study, we evaluate the safety and efficacy of TCAR in patients with symptomatic internal carotid artery disease compared with carotid endarterectomy (CEA) and carotid artery stenting (CAS).

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.

Artificially Intelligent Billing in Spine Surgery: An Analysis of a Large Language Model.

Study Design: Retrospective cohort study.

Objectives: This study assessed the effectiveness of a popular large language model, ChatGPT-4, in predicting Current Procedural Terminology (CPT) codes from surgical operative notes. By employing a combination of prompt engineering, natural language processing (NLP), and machine learning techniques on standard operative notes, the study sought to enhance billing efficiency, optimize revenue collection, and reduce coding errors.

An 11-Year-Long Analysis of the Risks Associated With Age in Patients Undergoing Anterior Cervical Discectomy and Fusion in a Large, Urban Academic Hospital.

Study Design: A retrospective database study of patients at an urban academic medical center undergoing an Anterior Cervical Discectomy and Fusion (ACDF) surgery between 2008 and 2019.

Objective: ACDF is one of the most common spinal procedures. Old age has been found to be a common risk factor for postoperative complications across a plethora of spine procedures.