Outcomes of Revision Cartilage Restoration Surgery for Failed Primary Treatment of Chondral or Osteochondral Defects of the Knee: A Systematic Review.

Background: Failure of primary cartilage restoration procedures of the knee that proceed to necessitating revision cartilage procedures represent a challenging clinical scenario with variable outcomes reported in previous literature.

Purpose: To perform a systematic review and meta-analysis of clinical outcomes and adverse events after revision cartilage restoration procedures of the knee for failed primary cartilage procedures.

Study Design: Systematic review and meta-analysis; Level of evidence, 4.

AI-Augmented Kidney Stone Composition Analysis with Auto-Release Improves Quality, Efficiency, Cost-Effectiveness, and Staff Satisfaction.

Background: We sought to evaluate key performance indicators related to an internally developed and deployed artificial intelligence (AI)-augmented kidney stone composition test system for potential improvements in test quality, efficiency, cost-effectiveness, and staff satisfaction.

Methods: We compared quality, efficiency, staff satisfaction, and financial data from the 6 months after the AI-augmented laboratory test system was deployed (test period) with data from the same 6-month period in the previous year (control period) to determine if AI-augmentation improved key performance indicators of this laboratory test.

Results: In the 6 months following the deployment (test period) of the AI-augmented kidney stone composition test system, 44 830 kidney stones were analyzed.

Large Language Models Applied to Health Care Tasks May Improve Clinical Efficiency, Value of Care Rendered, Research, and Medical Education.

Large language models (LLMs) are generative artificial intelligence models that create content on the basis of the data on which it was trained. Processing capabilities have evolved from text only to being multimodal including text, images, audio, and video features. In health care settings, LLMs are being applied to several clinically important areas, including patient care and workflow efficiency, communications, hospital operations and data management, medical education, practice management, and health care research.

Effectiveness of a large language model for clinical information retrieval regarding shoulder arthroplasty.

Purpose: To determine the scope and accuracy of medical information provided by ChatGPT-4 in response to clinical queries concerning total shoulder arthroplasty (TSA), and to compare these results to those of the Google search engine.

Methods: A patient-replicated query for 'total shoulder replacement' was performed using both Google Web Search (the most frequently used search engine worldwide) and ChatGPT-4. The top 10 frequently asked questions (FAQs), answers, and associated sources were extracted.

Ethical Application of Generative Artificial Intelligence in Medicine.

Generative artificial intelligence (AI) may revolutionize health care, providing solutions that range from enhancing diagnostic accuracy to personalizing treatment plans. However, its rapid and largely unregulated integration into medicine raises ethical concerns related to data integrity, patient safety, and appropriate oversight. One of the primary ethical challenges lies in generative AI's potential to produce misleading or fabricated information, posing risks of misdiagnosis or inappropriate treatment recommendations, which underscore the necessity for robust physician oversight.

Return to Sport in Professional Athletes After Cartilage Restoration Surgery of the Knee: A Systematic Review and Meta-Analysis Demonstrates Gender Inequality and the Need for Improved Reporting.

Objective: To synthesize the literature concerning return to sport (RTS) and related outcomes after cartilage restoration surgery of the knee in professional athletes.

Design: Cochrane, PubMed, and OVID/Medline databases were queried for data pertaining to RTS after knee cartilage surgery in professional athletes. Demographic information, cartilage lesion characteristics, and RTS-specific information were extracted.

Applications of Artificial Intelligence for Health Care Providers.

Recent research shows that physicians lack the knowledge and ability to use artificial intelligence (AI) effectively. We thus introduce a new series of articles, "Applications of Artificial Intelligence for Health Care Providers." Like the arthroscope, AI is a powerful tool, and we must adapt our skills to effectively incorporate and apply this tool in our practices.

Favorable Outcomes Following Elbow Ulnar Collateral Ligament Reconstruction and Repair, Though Repair Associated with Increased Risk of Revision Surgery.

Purpose: To assess the risk of revision surgery following repair versus reconstruction of the medial ulnar collateral ligament (UCL) of the elbow in a national sample of patients in the United States.

Methods: This was a retrospective cohort study of young patients (≤35 years old) who underwent primary UCL reconstruction or repair for an isolated medial UCL injury of the elbow from October 2015 through October 2022 in a large national database (PearlDiver). Patient demographic data, comorbidities, surgical details, and concomitant ulnar nerve procedures were collected.

Diagnostic performance of deep learning for leg length measurements on radiographs in leg length discrepancy: A systematic review.

Purpose: To systematically review the literature regarding machine learning in leg length discrepancy (LLD) and to provide insight into the most relevant manuscripts on this topic in order to highlight the importance and future clinical implications of machine learning in the diagnosis and treatment of LLD.

Methods: A systematic electronic search was conducted using PubMed, OVID/Medline and Cochrane libraries in accordance with Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines. Two observers independently screened the abstracts and titles of potential articles.

Machine Learning Algorithms Can Be Reliably Leveraged to Identify Patients at High Risk of Prolonged Postoperative Opioid Use Following Orthopedic Surgery: A Systematic Review.

Machine learning (ML) has emerged as a method to determine patient-specific risk for prolonged postoperative opioid use after orthopedic procedures. : We sought to analyze the efficacy and validity of ML algorithms in identifying patients who are at high risk for prolonged opioid use following orthopedic procedures. : PubMed, EMBASE, and Web of Science Core Collection databases were queried for articles published prior to August 2021 for articles applying ML to predict prolonged postoperative opioid use following orthopedic surgeries.

Do Overall Weight, Body Mass Index, or Clinically Significant Weight Changes Occur After Total Joint Arthroplasty? A Meta-Analysis of 60,837 Patients.

Background: Total joint arthroplasty (TJA) is well-recognized for improving quality of life and functional outcomes of patients with osteoarthritis; however, TJA's impact on body weight remains unclear. Recent trends have demonstrated a shift among TJA patients, such that patients who have higher body mass indices (BMIs) are undergoing this common surgery. Given this trend, it is critical to characterize the impact TJA has on body weight or BMI.

A machine learning analysis of patient and imaging factors associated with achieving clinically substantial outcome improvements following total shoulder arthroplasty: Implications for selecting anatomic or reverse prostheses.

Background: Indications for reverse total shoulder arthroplasty(rTSA) continue to expand making it challenging to predict whether patients will benefit more from anatomic TSA(aTSA) or rTSA. The purpose of this study was to determine which factors differ between aTSA and rTSA patients that achieve meaningful outcomes and may influence surgical indication.

Methods: Random Forest dimensionality reduction was applied to reduce 23 features into a model optimizing substantial clinical benefit (SCB) prediction of the American Shoulder and Elbow Surgeon score using 1117 consecutive patients with 2-year follow up.

Evaluation of myocarditis with a free-breathing three-dimensional isotropic whole-heart joint T1 and T2 mapping sequence.

Background: The diagnosis of myocarditis by cardiovascular magnetic resonance (CMR) requires the use of T2 and T1 weighted imaging, ideally incorporating parametric mapping. Current two-dimensional (2D) mapping sequences are acquired sequentially and involve multiple breath-holds resulting in prolonged scan times and anisotropic image resolution. We developed an isotropic free-breathing three-dimensional (3D) whole-heart sequence that allows simultaneous T1 and T2 mapping and validated it in patients with suspected myocarditis.

Image navigator-based, automated coronary magnetic resonance angiography for the detection of coronary artery stenosis.

Background: Coronary computed tomography angiography (CCTA) is recommended as the first-line diagnostic imaging modality in low-to-intermediate-risk individuals suspected of stable coronary artery disease (CAD). However, CCTA exposes patients to ionizing radiation and potentially nephrotoxic contrast agents. Invasive coronary angiography is the gold-standard investigation to guide coronary revascularisation strategy; however, invasive procedures incur an inherent risk to the patient.

Editorial Commentary: The Scope of Medical Research Concerning ChatGPT Remains Limited by Lack of Originality.

There is no shortage of literature surrounding ChatGPT and whether this large language model can provide accurate and clinically relevant information in response to simulated patient queries. Unfortunately, there is a shortage of literature addressing important considerations beyond these experimental and entertaining uses. Indeed, a trend for redundancy has emerged where most of the literature has applied ChatGPT to the same tasks while simply swapping the subject matter, resulting in a failure to expand the impact and reach of this potentially transformational artificial intelligence (AI) solution.

Fast, automated, real-time 3D passive balloon catheter tracking during MRI-guided cardiac catheterization using orthogonal projection imaging and real-time image-based catheter detection.

Purpose: MRI-guidance of cardiac catheterization is currently performed using one or multiple 2D imaging planes, which may be suboptimal for catheter navigation, especially in patients with complex anatomies. The purpose of the work was to develop a robust real-time 3D catheter tracking method and 3D visualization strategy for improved MRI-guidance of cardiac catheterization procedures.

Methods: A fast 3D tracking technique was developed using continuous acquisition of two orthogonal 2D-projection images.

Currently Available Large Language Models Do Not Provide Musculoskeletal Treatment Recommendations That Are Concordant With Evidence-Based Clinical Practice Guidelines.

Purpose: To determine whether several leading, commercially available large language models (LLMs) provide treatment recommendations concordant with evidence-based clinical practice guidelines (CPGs) developed by the American Academy of Orthopaedic Surgeons (AAOS).

Methods: All CPGs concerning the management of rotator cuff tears (n = 33) and anterior cruciate ligament injuries (n = 15) were extracted from the AAOS. Treatment recommendations from Chat-Generative Pretrained Transformer version 4 (ChatGPT-4), Gemini, Mistral-7B, and Claude-3 were graded by 2 blinded physicians as being concordant, discordant, or indeterminate (i.

Remote sensing for estimating genetic parameters of biomass accumulation and modeling stability of growth curves in alfalfa.

Multispectral imaging by unoccupied aerial vehicles provides a nondestructive, high-throughput approach to measure biomass accumulation over successive alfalfa (Medicago sativa L. subsp. sativa) harvests.

Fast cardiac T mapping using slice-selective adiabatic spin-lock preparation pulses.

Purpose: Myocardial T mapping techniques commonly acquire multiple images in one breathhold to calculate a single-slice T map. Recently, non-selective adiabatic pulses have been used for robust spin-lock preparation (T). The objective of this study was to develop a fast multi-slice myocardial T mapping approach.

Defining clinically relevant outcome thresholds for pain and function after osteochondral autograft transplantation of the knee.

Purpose: To define the minimal clinically important difference (MCID) for measures of pain and function at 2, 5 and 10 years after osteochondral autograft transplantations (OATs).

Methods: Patients undergoing OATs of the knee were identified from a prospectively maintained cartilage surgery registry. Baseline demographic, injury and surgical factors were collected.

The Importance of Geographic Proximity of Convalescent Plasma Donors.

Donor-recipient proximity emerged as an important factor influencing the efficacy of COVID-19 convalescent plasma (CCP) treatment during the early stages of the COVID-19 pandemic. This relationship was uncovered while analyzing data collected in the collaborative Expanded Access Program (EAP) for CCP at Mayo Clinic, a project aimed to establish protocols for CCP use amid the uncertainty of the novel disease. Analysis of data from nearly 28,000 patients revealed a significant reduction in risk of 30-day mortality for those receiving near-sourced plasma when compared to those receiving distantly sourced plasma [pooled relative risk, 0.

Cardiovascular magnetic resonance reveals myocardial involvement in patients with active stage of inflammatory bowel disease.

Background: Active inflammatory bowel disease (A-IBD) but not remission (R-IBD) has been associated with an increased risk of cardiovascular death and hospitalization for heart failure.

Objectives: Using cardiovascular magnetic resonance (CMR), this study aims to assess adverse myocardial remodeling in patients with IBD in correlation with disease activity.

Methods: Forty-four IBD patients without cardiovascular disease (24 female, median-age: 39.