Editorial Commentary: Artificial Intelligence Models Show Impressive Results for Musculoskeletal Pathology Detection.

An important domain of artificial intelligence is deep learning, which comprises computed vision tasks used for recognizing complex patterns in orthopaedic imaging, thus automating the identification of pathology. Purported benefits include an expedited clinical workflow; improved performance and consistency in diagnostic tasks; decreased time allocation burden; augmentation of diagnostic performance, decreased inter-reader discrepancies in measurements and diagnosis as a function of reducing subjectivity in the setting of differences in imaging quality, resolution, penetrance, or orientation; and the ability to function autonomously without rest (unlike human observers). Detection may include the presence or absence of an entity or identification of a specific landmark. Within the field of musculoskeletal health, such capabilities have been shown across a wide range of tasks such as detecting the presence or absence of a rotator cuff tear or automatically identifying the center of the hip joint. The clinical relevance and success of these research endeavors have led to a plethora of novel algorithms. However, few of these algorithms have been externally validated, and evidence remains inconclusive as to whether they provide a diagnostic benefit when compared with the current, human gold standard.

Medical Imaging Applications Developed Using Artificial Intelligence Demonstrate High Internal Validity Yet Are Limited in Scope and Lack External Validation.

PURPOSE: To (1) review definitions and concepts necessary to interpret applications of deep learning (DL; a domain of artificial intelligence that leverages neural networks to make predictions on media inputs such as images) and (2) identify knowledge and translational gaps in the literature to provide insight into specific areas for improvement as adoption of this technology continues. METHODS: A comprehensive search of the literature was performed in December 2023 for articles regarding the use of DL in sports medicine. For each study, information regarding the joint of focus, specific anatomic structure/pathology to which DL was applied, imaging modality utilized, source of images used for model training and testing, data set size, model performance, and whether the DL model was externally validated was recorded. A numerical scale was used to rate each DL model's clinical impact, with 1 corresponding to proof-of-concept studies with little to no direct clinical impact and 5 corresponding to practice-changing clinical impact and ready for clinical deployment. RESULTS: Fifty-five studies were identified, all of which were published within the past 5 years, while 82% were published within the past 3 years. Of the DL models identified, 84% were developed for classification tasks, 9% for automated measurements, and 7% for segmentation. A total of 62% of studies utilized magnetic resonance imaging as the imaging modality, 25% radiographs, and 7% ultrasound, while 1 study each used computed tomography, arthroscopic images, or arthroscopic video. Sixty-five percent of studies focused on the detection of tears (anterior cruciate ligament [ACL], rotator cuff [RC], and meniscus). The diagnostic performance of ACL tears, as determined by the area under the receiver operator curve (AUROC), ranged from 0.81 to 0.99 for ACL tears (excellent to near perfect), 0.83 to 0.94 for RC tears (excellent), and from 0.75 to 0.96 for meniscus tears (acceptable to excellent). In addition, 3 studies focused on detection of cartilage lesions had AUROC ranging from 0.90 to 0.92 (excellent performance). However, only 4 (7%) studies externally validated their models, suggesting that they may not be generalizable or may not perform well when applied to populations other than that used to develop the model. Finally, the mean clinical impact score was 2 (range, 1-3) on scale of 1 to 5, corresponding to limited clinical applicability. CONCLUSIONS: DL models in orthopaedic sports medicine show generally excellent performance (high internal validity) but require external validation to facilitate clinical deployment. In addition, current models have low clinical applicability and fail to advance the field due to a focus on routine tasks and a narrow conceptual framework. LEVEL OF EVIDENCE: Level IV, scoping review of Level I to IV studies.

The Large Language Model ChatGPT-4 Demonstrates Excellent Triage Capabilities and Diagnostic Performance for Patients Presenting with Various Causes of Knee Pain.

PURPOSE: To provide a proof-of-concept analysis of the appropriateness and performance of ChatGPT-4 to triage, synthesize differential diagnoses, and generate treatment plans concerning common presentations of knee pain. METHODS: Twenty knee complaints warranting triage and expanded scenarios were input into ChatGPT-4, with memory cleared prior to each new input to mitigate bias. For the 10 triage complaints, ChatGPT-4 was asked to generate a differential diagnosis which was graded for accuracy and suitability in comparison to a differential created by two orthopaedic sports medicine physicians. For the 10 clinical scenarios, ChatGPT-4 was prompted to provide treatment guidance for the patient, which was again graded. To test the higher-order capabilities of ChatGPT-4, further inquiry into these specific management recommendations was performed and graded. RESULTS: All ChatGPT-4 diagnoses were deemed appropriate within the spectrum of potential pathologies on a differential. The top diagnosis on the differential was identical between surgeons and ChatGPT-4 for 70% of scenarios, and the top diagnosis provided by the surgeon appeared as either the first or second diagnosis in 90% of scenarios. Overall, 16/30 (53.3%) of diagnoses in the differential were identical. When provided with 10 expanded vignettes with a single diagnosis, the accuracy of ChatGPT-4 increased to 100%, with the suitability of management graded as appropriate in 90% of cases. Specific information pertaining to conservative management, surgical approaches, and related treatments was appropriate and accurate in 100% of cases. CONCLUSION: ChatGPT-4 provided clinically reasonable diagnoses to triage patient complaints of knee pain due to various underlying conditions that was generally consistent with differentials provided by sports medicine physicians. Diagnostic performance was enhanced when providing additional information, allowing ChatGPT-4 to reach high predictive accuracy for recommendations concerning management and treatment options. However, ChatGPT-4 may demonstrate clinically important error rates for diagnosis depending on prompting strategy and information provided; therefore, further are necessary to prior to implementation into clinical workflows.

Hip Arthroscopy Simulator Training With Immersive Virtual Reality Has Similar Effectiveness to Nonimmersive Virtual Reality.

PURPOSE: To (1) compare the efficacy of immersive virtual reality (iVR) to nonimmersive virtual reality (non-iVR) training in hip arthroscopy on procedural and knowledge-based skills acquisition and (2) evaluate the relative cost of each platform. METHODS: Fourteen orthopaedic surgery residents were randomized to simulation training utilizing an iVR Hip Arthroscopy Simulator (n = 7; PrecisionOS) or non-iVR simulator (n = 7; ArthroS Hip VR; VirtaMed). After training, performance was assessed on a cadaver by 4 expert hip arthroscopists through arthroscopic video review of a diagnostic hip arthroscopy. Performance was assessed using the Objective Structured Assessment of Technical Skills (OSATS) and Arthroscopic Surgery Skill Evaluation Tool (ASSET) scores. A cost analysis was performed using the transfer effectiveness ratio (TER) and a direct cost comparison of iVR to non-iVR. RESULTS: Demographic characteristics did not differ between treatment arms or by training level, hip arthroscopy experience, or prior simulator use. No significant differences were observed in OSATS and ASSET scores between iVR and non-iVR cohorts (OSATS: iVR 19.6 ± 4.4, non-iVR 21.0 ± 4.1, P = .55; ASSET: iVR 23.7 ± 4.5, non-iVR 25.8 ± 4.8, P = .43). The absolute TER was 0.06 and there was a 132-fold cost difference of iVR to non-iVR. CONCLUSIONS: Hip arthroscopy simulator training with iVR had similar performance results to non-iVR for technical skill and procedural knowledge acquisition after expert arthroscopic video assessment. The iVR platform had similar effectiveness in transfer of skill compared to non-iVR with a 132 times cost differential. CLINICAL RELEVANCE: Due to the accessibility, effectiveness, and relative affordability, iVR training may be beneficial in the future of safe arthroscopic hip training.

ChatGPT-4 Performs Clinical Information Retrieval Tasks Utilizing Consistently More Trustworthy Resources Than Does Google Search for Queries Concerning the Latarjet Procedure.

PURPOSE: To assess the ability for ChatGPT-4, an automated Chatbot powered by artificial intelligence (AI), to answer common patient questions concerning the Latarjet procedure for patients with anterior shoulder instability and compare this performance to Google Search Engine. METHODS: Using previously validated methods, a Google search was first performed using the query "Latarjet." Subsequently, the top ten frequently asked questions (FAQs) and associated sources were extracted. ChatGPT-4 was then prompted to provide the top ten FAQs and answers concerning the procedure. This process was repeated to identify additional FAQs requiring discrete-numeric answers to allow for a comparison between ChatGPT-4 and Google. Discrete, numeric answers were subsequently assessed for accuracy based on the clinical judgement of two fellowship-trained sports medicine surgeons blinded to search platform. RESULTS: Mean (±standard deviation) accuracy to numeric-based answers were 2.9±0.9 for ChatGPT-4 versus 2.5±1.4 for Google (p=0.65). ChatGPT-4 derived information for answers only from academic sources, which was significantly different from Google Search Engine (p=0.003), which used only 30% academic sources and websites from individual surgeons (50%) and larger medical practices (20%). For general FAQs, 40% of FAQs were found to be identical when comparing ChatGPT-4 and Google Search Engine. In terms of sources used to answer these questions, ChatGPT-4 again used 100% academic resources, while Google Search Engine used 60% academic resources, 20% surgeon personal websites, and 20% medical practices (p=0.087). CONCLUSION: ChatGPT-4 demonstrated the ability to provide accurate and reliable information about the Latarjet procedure in response to patient queries, using multiple academic sources in all cases. This was in contrast to Google Search Engine, which more frequently used single surgeon and large medical practice websites. Despite differences in the resources accessed to perform information retrieval tasks, the clinical relevance and accuracy of information provided did not significantly differ between ChatGPT-4 and Google Search Engine.

Diagnostic Performance of Artificial Intelligence for Detection of Scaphoid and Distal Radius Fractures: A Systematic Review.

PURPOSE: To review the existing literature to (1) determine the diagnostic efficacy of artificial intelligence (AI) models for detecting scaphoid and distal radius fractures and (2) compare the efficacy to human clinical experts. METHODS: PubMed, OVID/Medline, and Cochrane libraries were queried for studies investigating the development, validation, and analysis of AI for the detection of scaphoid or distal radius fractures. Data regarding study design, AI model development and architecture, prediction accuracy/area under the receiver operator characteristic curve (AUROC), and imaging modalities were recorded. RESULTS: A total of 21 studies were identified, of which 12 (57.1%) used AI to detect fractures of the distal radius, and nine (42.9%) used AI to detect fractures of the scaphoid. AI models demonstrated good diagnostic performance on average, with AUROC values ranging from 0.77 to 0.96 for scaphoid fractures and from 0.90 to 0.99 for distal radius fractures. Accuracy of AI models ranged between 72.0% to 90.3% and 89.0% to 98.0% for scaphoid and distal radius fractures, respectively. When compared to clinical experts, 13 of 14 (92.9%) studies reported that AI models demonstrated comparable or better performance. The type of fracture influenced model performance, with worse overall performance on occult scaphoid fractures; however, models trained specifically on occult fractures demonstrated substantially improved performance when compared to humans. CONCLUSIONS: AI models demonstrated excellent performance for detecting scaphoid and distal radius fractures, with the majority demonstrating comparable or better performance compared with human experts. Worse performance was demonstrated on occult fractures. However, when trained specifically on difficult fracture patterns, AI models demonstrated improved performance. CLINICAL RELEVANCE: AI models can help detect commonly missed occult fractures while enhancing workflow efficiency for distal radius and scaphoid fracture diagnoses. As performance varies based on fracture type, future studies focused on wrist fracture detection should clearly define whether the goal is to (1) identify difficult-to-detect fractures or (2) improve workflow efficiency by assisting in routine tasks.

The Reverse Fragility Index for Mortality End Points in Randomized Trials Comparing Uncemented and Cemented Hemiarthroplasty for Intracapsular Hip Fractures.

BACKGROUND: Interpreting clinical relevance of randomized clinical trials (RCTs) is challenging when P-values are marginally above or below the P = .05 threshold. This study examined the robustness of statistically insignificant mortality events from RCTs comparing hemiarthroplasty femoral fixation for displaced intracapsular hip fractures through the reverse fragility index (RFI). METHODS: RCTs were identified using Pubmed, OVID/Medline, and Cochrane databases. Mortality endpoints were stratified into 3 categories: (1) within 30-days, (2) within 90-days, and (3) at latest follow-up. The RFI was derived by manipulating reported mortality events utilizing a contingency table while maintaining a constant number of participants. The reverse fragility quotient (RFQ) was quantified by dividing the RFI by the study sample. RESULTS: Eight RCTs (2,494 participants) were included. The median RFI and RFQ within 30-days was 3.0 (interquartile range [IQR]: 3.0 to 6.0) and 0.016 (IQR: 0.015 to 0.021), suggesting nonsignificant findings were contingent on 1.6 mortality events/100 participants. The median RFI and RFQ within 90-days was 6.0 (IQR: 4.0 to 7.0) and 0.028 (IQR: 0.024 to 0.038), suggesting nonsignificant findings were contingent on 2.8 mortality events/100 participants. At latest follow-up, the median RFI and RFQ was 7.0 (IQR: 6.0 to 12.0) and 0.038 (IQR: 0.029 to 0.054), suggesting nonsignificant findings were contingent on only 3.8 mortality events/100 participants. Median loss to follow-up was 16.0 (IQR: 11.0 to 58.0; 228% greater than RFI), and exceeded the RFI in 6/7(85.7%) studies. CONCLUSIONS: A small number of events (median of 7) was required to convert a statistically nonsignificant finding to one that is significant for the endpoint of mortality. The median loss to follow-up exceeded the median RFI by greater than 200%, suggesting methodological limitations such as patient allocation could alter conclusions.

Deep-Learning Automation of Preoperative Radiographic Parameters Associated With Early Periprosthetic Femur Fracture After Total Hip Arthroplasty.

BACKGROUND: The radiographic assessment of bone morphology impacts implant selection and fixation type in total hip arthroplasty (THA) and is important to minimize the risk of periprosthetic femur fracture (PFF). We utilized a deep-learning algorithm to automate femoral radiographic parameters and determined which automated parameters were associated with early PFF. METHODS: Radiographs from a publicly available database and from patients undergoing primary cementless THA at a high-volume institution (2016 to 2020) were obtained. A U-Net algorithm was trained to segment femoral landmarks for bone morphology parameter automation. Automated parameters were compared against that of a fellowship-trained surgeon and compared in an independent cohort of 100 patients who underwent THA (50 with early PFF and 50 controls matched by femoral component, age, sex, body mass index, and surgical approach). RESULTS: On the independent cohort, the algorithm generated 1,710 unique measurements for 95 images (5% lesser trochanter identification failure) in 22 minutes. Medullary canal width, femoral cortex width, canal flare index, morphological cortical index, canal bone ratio, and canal calcar ratio had good-to-excellent correlation with surgeon measurements (Pearson's correlation coefficient: 0.76 to 0.96). Canal calcar ratios (0.43 ± 0.08 versus 0.40 ± 0.07) and canal bone ratios (0.39 ± 0.06 versus 0.36 ± 0.06) were higher (P < .05) in the PFF cohort when comparing the automated parameters. CONCLUSIONS: Deep-learning automated parameters demonstrated differences in patients who had and did not have early PFF after cementless primary THA. This algorithm has the potential to complement and improve patient-specific PFF risk-prediction tools.

Machine Learning on Medicare Claims Poorly Predicts the Individual Risk of 30-Day Unplanned Readmission After Total Joint Arthroplasty, Yet Uncovers Interesting Population-level Associations With Annual Procedure Volumes.

BACKGROUND: Unplanned hospital readmissions after total joint arthroplasty (TJA) represent potentially serious adverse events and remain a critical measure of hospital quality. Predicting the risk of readmission after TJA may provide patients and clinicians with valuable information for preoperative decision-making. QUESTIONS/PURPOSES: (1) Can nonlinear machine-learning models integrating preoperatively available patient, surgeon, hospital, and county-level information predict 30-day unplanned hospital readmissions in a large cohort of nationwide Medicare beneficiaries undergoing TJA? (2) Which predictors are the most important in predicting 30-day unplanned hospital readmissions? (3) What specific information regarding population-level associations can we obtain from interpreting partial dependency plots (plots describing, given our modeling choice, the potentially nonlinear shape of associations between predictors and readmissions) of the most important predictors of 30-day readmission? METHODS: National Medicare claims data (chosen because this database represents a large proportion of patients undergoing TJA annually) were analyzed for patients undergoing inpatient TJA between October 2016 and September 2018. A total of 679,041 TJAs (239,391 THAs [61.3% women, 91.9% White, 52.6% between 70 and 79 years old] and 439,650 TKAs [63.3% women, 90% White, 55.2% between 70 and 79 years old]) were included. Model features included demographics, county-level social determinants of health, prior-year (365-day) hospital and surgeon TJA procedure volumes, and clinical classification software-refined diagnosis and procedure categories summarizing each patient's Medicare claims 365 days before TJA. Machine-learning models, namely generalized additive models with pairwise interactions (prediction models consisting of both univariate predictions and pairwise interaction terms that allow for nonlinear effects), were trained and evaluated for predictive performance using area under the receiver operating characteristic (AUROC; 1.0 = perfect discrimination, 0.5 = no better than random chance) and precision-recall curves (AUPRC; equivalent to the average positive predictive value, which does not give credit for guessing "no readmission" when this is true most of the time, interpretable relative to the base rate of readmissions) on two holdout samples. All admissions (except the last 2 months' worth) were collected and split randomly 80%/20%. The training cohort was formed with the random 80% sample, which was downsampled (so it included all readmissions and a random, equal number of nonreadmissions). The random 20% sample served as the first test cohort ("random holdout"). The last 2 months of admissions (originally held aside) served as the second test cohort ("2-month holdout"). Finally, feature importances (the degree to which each variable contributed to the predictions) and partial dependency plots were investigated to answer the second and third research questions. RESULTS: For the random holdout sample, model performance values in terms of AUROC and AUPRC were 0.65 and 0.087, respectively, for THA and 0.66 and 0.077, respectively, for TKA. For the 2-month holdout sample, these numbers were 0.66 and 0.087 and 0.65 and 0.075. Thus, our nonlinear models incorporating a wide variety of preoperative features from Medicare claims data could not well-predict the individual likelihood of readmissions (that is, the models performed poorly and are not appropriate for clinical use). The most predictive features (in terms of mean absolute scores) and their partial dependency graphs still confer information about population-level associations with increased risk of readmission, namely with older patient age, low prior 365-day surgeon and hospital TJA procedure volumes, being a man, patient history of cardiac diagnoses and lack of oncologic diagnoses, and higher county-level rates of hospitalizations for ambulatory-care sensitive conditions. Further inspection of partial dependency plots revealed nonlinear population-level associations specifically for surgeon and hospital procedure volumes. The readmission risk for THA and TKA decreased as surgeons performed more procedures in the prior 365 days, up to approximately 75 TJAs (odds ratio [OR] = 1.2 for TKA and 1.3 for THA), but no further risk reduction was observed for higher annual surgeon procedure volumes. For THA, the readmission risk decreased as hospitals performed more procedures, up to approximately 600 TJAs (OR = 1.2), but no further risk reduction was observed for higher annual hospital procedure volumes. CONCLUSION: A large dataset of Medicare claims and machine learning were inadequate to provide a clinically useful individual prediction model for 30-day unplanned readmissions after TKA or THA, suggesting that other factors that are not routinely collected in claims databases are needed for predicting readmissions. Nonlinear population-level associations between low surgeon and hospital procedure volumes and increased readmission risk were identified, including specific volume thresholds above which the readmission risk no longer decreases, which may still be indirectly clinically useful in guiding policy as well as patient decision-making when selecting a hospital or surgeon for treatment. LEVEL OF EVIDENCE: Level III, therapeutic study.

Editorial Commentary: Recognizing and Avoiding Medical Misinformation Across Digital Platforms: Smoke, Mirrors (and Streaming).

The evolution of social media and related online sources has substantially increased the ability of patients to query and access publicly available information that may have relevance to a potential musculoskeletal condition of interest. Although increased accessibility to information has several purported benefits, including encouragement of patients to become more invested in their care through self-teaching, a downside to the existence of a vast number of unregulated resources remains the risk of misinformation. As health care providers, we have a moral and ethical obligation to mitigate this risk by directing patients to high-quality resources for medical information and to be aware of resources that are unreliable. To this end, a growing body of evidence has suggested that YouTube lacks reliability and quality in terms of medical information concerning a variety of musculoskeletal conditions.

A Guide for the Application of Statistics in Biomedical Studies Concerning Machine Learning and Artificial Intelligence.

With the plethora of machine learning (ML) analyses published in the orthopaedic literature within the last 5 years, several attempts have been made to enhance our understanding of what exactly ML means and how it is used. At its most fundamental level, ML comprises a branch of artificial intelligence that uses algorithms to analyze and learn from patterns in data without explicit programming or human intervention. On the other hand, traditional statistics require a user to specifically choose variables of interest to create a model capable of predicting an outcome, the output of which (1) may be falsely influenced by the variables chosen to be included by the user and (2) does not allow for optimization of performance. Early publications have served as succinct editorials or reviews intended to ease audiences unfamiliar with ML into the complexities that accompany the subject. Most commonly, the focus of these studies concerns the terminology and concepts surrounding ML because it is important to understand the rationale behind performing such studies. Unfortunately, these publications only touch on the most basic aspects of ML and are too frequently repetitive. Indeed, the conclusion of these articles reiterate that the potential clinical utility of these algorithms remains tangential at best in their current form and caution against premature adoption without external validation. By doing so, our perspective and ability to draw our own conclusions from these studies have not advanced, and we are left concluding with each subsequent study that a new algorithm is published for an outcome of interest that cannot be used until further validation. What readers now need is to regress to embrace the principles of the scientific method that they have used to critically assess vast numbers of publications before this wave of newly applied statistical methodology-a guide to interpret results such that their own conclusions can be drawn. LEVEL OF EVIDENCE: Level V, expert opinion.

Over One-Third of Patients With Multiligament Knee Injuries and an Intact Anterior Cruciate Ligament Demonstrate Medial Meniscal Ramp Lesions on Magnetic Resonance Imaging.

PURPOSE: To determine the incidence of ramp lesions and posteromedial tibial plateau (PMTP) bone bruising on magnetic resonance imaging (MRI) in patients with multiligament knee injuries (MLKIs) and an intact anterior cruciate ligament (ACL). METHODS: A retrospective review of consecutive patients surgically treated for MLKIs at 2 level I trauma centers between January 2001 and March 2021 was performed. Only MLKIs with an intact ACL that received MRI scans within 90 days of the injury were included. All MLKIs were diagnosed on MRI and confirmed with operative reports. Two musculoskeletal radiologists retrospectively rereviewed preoperative MRIs for evidence of medial meniscus ramp lesions (MMRLs) and PMTP bone bruises using previously established classification systems. Intraclass correlation coefficients were used to calculate the reliability between the radiologists. The incidence of MMRLs and PMTP bone bruises was quantified using descriptive statistics. RESULTS: A total of 221 MLKIs were identified, of which 32 (14.5%) had an intact ACL (87.5% male; mean age of 29.9 ± 8.6 years) and were included. The most common MLKI pattern was combined injury to the posterior cruciate ligament and posterolateral corner (n = 27, 84.4%). PMTP bone bruises were observed in 12 of 32 (37.5%) patients. Similarly, MMRLs were diagnosed in 12 of 32 (37.5%) patients. A total of 8 of 12 (66.7%) patients with MMRLs demonstrated evidence PMTP bone bruising. CONCLUSIONS: Over one-third of MLKI patients with an intact ACL were diagnosed with MMRLs on MRI in this series. PMTP bone bruising was observed in 66.7% of patients with MMRLs, suggesting that increased vigilance for identifying MMRLs at the time of ligament reconstruction should be practiced in patients with this bone bruising pattern. LEVEL OF EVIDENCE: Level IV, retrospective case series.

No Difference in Complication or Reoperation Rates Between Arthroscopic and Open Debridement for Lateral Epicondylitis: A National Database Study.

PURPOSE: To compare complication rates and 5-year reoperation rates between open debridement (OD) and arthroscopic debridement (AD) for lateral epicondylitis. METHODS: The PearlDiver MUExtr database (2010-2019) was reviewed for patients diagnosed with lateral epicondylitis (queried by International Classification of Diseases, Ninth Revision and International Classification of Diseases, Tenth Revision [ICD-10] codes) undergoing OD or AD of the common extensor tendon without repair (queried by Current Procedural Terminology codes). Patients were stratified into 2 cohorts: those who underwent AD and those who underwent OD. Nonoperative treatment modalities were reported for both groups within 1 year before index procedure. The rates of 90-day postoperative complications were compared, and multivariate logistic regression analysis was used to identify risk factors for complications. The 5-year reoperation rates, using laterality-specific ICD-10 codes, were also compared between the 2 groups. RESULTS: In total, 19,280 patients (OD = 17,139, AD = 2,141) were analyzed in this study. The most common nonoperative treatments for patients who underwent OD or AD were corticosteroid injections (49.5% vs 43.2%), physical therapy (24.8% vs 25.7%), bracing (2.8% vs 3.2%), and platelet-rich plasma injections (1.3% vs 1.0%). There were no significant differences in radial nerve injuries, hematomas, surgical site infections, wound dehiscence, and sepsis events between the 2 procedures (P = .50). The 5-year reoperation rate was not significantly different between the AD (5.0%) and OD (3.9%) cohorts (P = .10). CONCLUSIONS: For lateral epicondylitis, both AD and OD of the extensor carpi radialis brevis (without repair) were found to have low rates of 90-day adverse events, with no significant differences between the 2 approaches. Similarly, the 5-year reoperation rate was low and not statistically different for those treated with OD or AD. LEVEL OF EVIDENCE: Level III, cross-sectional study.

Increased Pitch Velocity Is Associated With Throwing Arm Kinetics, Injury Risk, and Ulnar Collateral Ligament Reconstruction in Adolescent, Collegiate, and Professional Baseball Pitchers: A Qualitative Systematic Review.

PURPOSE: To assess the relationship between pitch velocity and throwing arm kinetics, injury, and ulnar collateral ligament reconstruction (UCLr) among high school, collegiate, and professional baseball pitchers. METHODS: The Cochrane Database of Systematic Reviews, the Cochrane Central Register of Controlled Trials, PubMed (2008-2019), and OVID/MEDLINE (2008-2019) were queried for articles that reported on pitch velocity predicting throwing arm kinetics, injury, or UCLr. The Methodological Index for Non-randomized Studies checklist was used to evaluate the quality of all included studies. Descriptive statistics with ranges were used to quantify data where appropriate. RESULTS: A total of 24 studies examining 2,896 pitchers, with Level of Evidence II-V were included. Intergroup analysis noted pitch velocity was significantly correlated with elbow varus torque in high school (R2 = 0.36), collegiate (R2 = 0.29), and professional (R2 = 0.076) pitchers. Elbow distraction force was positively associated with ball velocity in interpitcher analyses of high school (R2 = 0.373), professional (R2 = 0.175), and mixed-cohort evaluations (R2 = 0.624). Intragroup analysis demonstrated a strong association between pitch velocity and elbow varus torque (R2 = 0.922-0.957) and elbow distraction force (R2 = 0.910) in professional pitchers. Faster ball velocity was positively associated with a history of throwing arm injury (R2 = 0.194) in nonadult pitchers. In 2 studies evaluating professionals, injured pitchers had faster pitch velocity before injury compared with uninjured controls (P = .014; P = .0354). The need for UCLr was positively correlated with pitch velocity (R2 = 0.036) in professional pitchers. The consequences of UCLr noted little to no decrease in pitch velocity. CONCLUSIONS: Professional baseball pitchers with faster pitch velocity may be at the greatest risk of elbow injury and subsequent UCLr, potentially through the mechanism of increased distractive forces on the medial elbow complex. When a pitcher ultimately undergoes UCLr, decreases in pitching performance are unlikely, but may occur, which should encourage pitchers to caution against maximizing pitch velocity. LEVEL OF EVIDENCE: Level IV, systematic review of Level II-IV studies.

A guide to appropriately planning and conducting meta-analyses-Part 1: indications, assumptions and understanding risk of bias.

A meta-analysis is the quantitative synthesis of data from two or more individual studies and are as a rule an important method of obtaining a more accurate estimate of the direction and magnitude of a treatment effect. However, it is imperative that the meta-analysis be performed with proper, rigorous methodology to ensure validity of the results and their interpretation. In this article the authors will review the most important questions researchers should consider when planning a meta-analysis to ensure proper indications and methodologies, minimize the risk of bias, and avoid misleading conclusions.

Lower donor site morbidity with hamstring and quadriceps tendon autograft compared with bone-patellar tendon-bone autograft after anterior cruciate ligament reconstruction: a systematic review and network meta-analysis of randomized controlled trials.

PURPOSE: To perform a meta-analysis of RCTs evaluating donor site morbidity after bone-patellar tendon-bone (BTB), hamstring tendon (HT) and quadriceps tendon (QT) autograft harvest for anterior cruciate ligament reconstruction (ACLR). METHODS: PubMed, OVID/Medline and Cochrane databases were queried in July 2022. All level one articles reporting the frequency of specific donor-site morbidity were included. Frequentist model network meta-analyses with P-scores were conducted to compare the prevalence of donor-site morbidity, complications, all-cause reoperations and revision ACLR among the three treatment groups. RESULTS: Twenty-one RCTs comprising the outcomes of 1726 patients were included. The overall pooled rate of donor-site morbidity (defined as anterior knee pain, difficulty/impossibility kneeling, or combination) was 47.3% (range, 3.8-86.7%). A 69% (95% confidence interval [95% CI]: 0.18-0.56) and 88% (95% CI: 0.04-0.33) lower odds of incurring donor-site morbidity was observed with HT and QT autografts, respectively (p < 0.0001, both), when compared to BTB autograft. QT autograft was associated with a non-statistically significant reduction in donor-site morbidity compared with HT autograft (OR: 0.37, 95% CI: 0.14-1.03, n.s.). Treatment rankings (ordered from best-to-worst autograft choice with respect to donor-site morbidity) were as follows: (1) QT (P-score = 0.99), (2) HT (P-score = 0.51) and (3) BTB (P-score = 0.00). No statistically significant associations were observed between autograft and complications (n.s.), reoperations (n.s.) or revision ACLR (n.s.). CONCLUSION: ACLR using HT and QT autograft tissue was associated with a significant reduction in donor-site morbidity compared to BTB autograft. Autograft selection was not associated with complications, all-cause reoperations, or revision ACLR. Based on the current data, there is sufficient evidence to recommend that autograft selection should be personalized through considering differential rates of donor-site morbidity in the context of patient expectations and activity level without concern for a clinically important change in the rate of adverse events. LEVEL OF EVIDENCE: Level I.

Radiographic findings involved in knee osteoarthritis progression are associated with pain symptom frequency and baseline disease severity: a population-level analysis using deep learning.

PURPOSE: To (1) develop a deep-learning (DL) algorithm capable of producing limb-length and knee-alignment measurements, and (2) determine the association between limb-length discrepancy (LLD), coronal-plane alignment, osteoarthritis (OA) severity, and patient-reported knee pain. METHODS: A multicenter, prospective patient cohort from the Osteoarthritis Initiative between 2004 and 2015 with full-limb standing radiographs at 12 month follow-up was included. A convolutional neural network was developed to automate measurements of the hip-knee-ankle (HKA) angle, femur, and tibia lengths, and LLD. At 12 month follow-up, patients reported their frequency of knee pain since enrollment and current level of knee pain. RESULTS: A total of 1011 patients (2022 knees, 52.3% female) with an average age of 61.2 ± 9.0 years were included. The algorithm performed 12,312 measurements in 5.4 h. ICC values of HKA and LLD ranged between 0.87 and 1.00 when compared against trained radiologist measurements. Knees producing pain most days of the month were significantly more varus (mean HKA:- 3.9° ± 2.8°) or valgus (mean HKA:2.8° ± 2.3°) compared to knees that did not produce any pain (p < 0.05). In varus knees, those producing pain on most days were part of the shorter limb compared to nonpainful knees (p < 0.05). Baseline Kellgren-Lawrence grade was significantly associated with HKA magnitude, LLD, and pain frequency at 12 month follow-up (p < 0.05 all). CONCLUSION: A higher frequency of knee pain was associated with more severe coronal plane deformity, with valgus deviation being one degree less than varus on average, suggesting that the knee tolerates less valgus deformation before symptoms become more consistent. Knee pain frequency was also associated with greater LLD and baseline KL grade, suggesting an association between radiographically apparent joint degeneration and pain frequency. LEVEL OF EVIDENCE: IV case series.

Choosing the appropriate measure of central tendency: mean, median, or mode?

Mean, median, and mode are among the most basic and consistently used measures of central tendency in statistical analysis and are crucial for simplifying data sets to a single value. However, there is a lack of understanding of when to use each metric and how various factors can impact these values. The aim of this article is to clarify some of the confusion related to each measure and explain how to select the appropriate metric for a given data set. The authors present this work as an educational resource, ensuring that these common statistical concepts are better understood throughout the Orthopedic research community.

A guide to appropriately planning and conducting meta-analyses: part 2-effect size estimation, heterogeneity and analytic approaches.

Meta-analyses by definition are a subtype of systematic review intended to quantitatively assess the strength of evidence present on an intervention or treatment. Such analyses may use individual-level data or aggregate data to produce a point estimate of an effect, also known as the combined effect, and measure precision of the calculated estimate. The current article will review several important considerations during the analytic phase of a meta-analysis, including selection of effect estimators, heterogeneity and various sub-types of meta-analytic approaches.

Establishing minimal detectable change thresholds for the international knee documentation committee and Kujala scores at one and two years after patellofemoral joint arthroplasty.

PURPOSE: To define the minimal detectable change (MDC) for the international knee documentation committee (IKDC) and Kujala scores one and two years after patellofemoral joint arthroplasty (PFA). METHODS: A distribution-based method (one-half the standard deviation of the mean difference between postoperative and preoperative outcome scores) was applied to establish MDC thresholds among 225 patients undergoing primary PFA at a single high-volume musculoskeletal-care center. Stability of change in MDC achievement was explored by quantifying the proportion of achievement at one- and two-year postoperative timepoints. Multivariable logistic regression analysis was performed to explore the association between sociodemographic and operative features on MDC achievement. RESULTS: MDC thresholds for the Kujala score were 10.3 (71.1% achievement) and 10.6 (70.4% achievement) at one- and two years, respectively. The MDC thresholds for the IKDC score were 11.2 (78.1% achievement) and 12.3 (69.0% achievement) at one- and two years, respectively. Predictors of achieving the MDC for the Kujala and IKDC scores at both time points were lower preoperative Kujala and IKDC scores, respectively. Preoperative thresholds of ≤ 24.1 and 7.6 for the Kujala and IKDC scores, respectively, were associated with a 90% MDC achievement probability. When preoperative thresholds approached 64.3 and 48.3 for the Kujala and IKDC, respectively, MDC achievement probability reduced to 50%. CONCLUSION: The MDC thresholds for the Kujala and IKDC scores two years after PFA were 10.6 and 12.3, respectively. Clinically significant health status changes were maintained overall, with a slight decrease in achievement rates between one and two years. MDC achievement was associated with disability at presentation, and several probability-based preoperative thresholds were defined. These findings may assist knee surgeons with patient selection and the decision to proceed with PFA by better understanding the patient-specific propensity for MDC achievement. LEVEL OF EVIDENCE: IV, retrospective case series.