Artificial Intelligence for Clinically Meaningful Outcome Prediction in Orthopedic Research: Current Applications and Limitations.

PURPOSE OF REVIEW: Patient-reported outcome measures (PROM) play a critical role in evaluating the success of treatment interventions for musculoskeletal conditions. However, predicting which patients will benefit from treatment interventions is complex and influenced by a multitude of factors. Artificial intelligence (AI) may better anticipate the propensity to achieve clinically meaningful outcomes through leveraging complex predictive analytics that allow for personalized medicine. This article provides a contemporary review of current applications of AI developed to predict clinically significant outcome (CSO) achievement after musculoskeletal treatment interventions. RECENT FINDINGS: The highest volume of literature exists in the subspecialties of total joint arthroplasty, spine, and sports medicine, with only three studies identified in the remaining orthopedic subspecialties combined. Performance is widely variable across models, with most studies only reporting discrimination as a performance metric. Given the complexity inherent in predictive modeling for this task, including data availability, data handling, model architecture, and outcome selection, studies vary widely in their methodology and results. Importantly, the majority of studies have not been externally validated or demonstrate important methodological limitations, precluding their implementation into clinical settings. A substantial body of literature has accumulated demonstrating variable internal validity, limited scope, and low potential for clinical deployment. The majority of studies attempt to predict the MCID-the lowest bar of clinical achievement. Though a small proportion of models demonstrate promise and highlight the utility of AI, important methodological limitations need to be addressed moving forward to leverage AI-based applications for clinical deployment.

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.

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.

Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence: A Critical Analysis Review.

¼ The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.¼ Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.¼ Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications. Not only may these applications be beneficial to patients but also to institutions and payors because they may inform potential cost expenditure, improve overall hospital efficiency, and help anticipate resource utilization.¼ AI infrastructure development requires institutional financial commitment and a team of clinicians and data scientists with expertise in AI that can complement skill sets and knowledge. Once a team is established and a goal is determined, teams (1) obtain, curate, and label data; (2) establish a reference standard; (3) develop an AI model; (4) evaluate the performance of the AI model; (5) externally validate the model, and (6) reinforce, improve, and evaluate the model's performance until clinical implementation is possible.¼ Understanding the implications of AI in orthopaedics may eventually lead to wide-ranging improvements in patient care. However, AI, while holding tremendous promise, is not without methodological and ethical limitations that are essential to address. First, it is important to ensure external validity of programs before their use in a clinical setting. Investigators should maintain high quality data records and registry surveillance, exercise caution when evaluating others' reported AI applications, and increase transparency of the methodological conduct of current models to improve external validity and avoid propagating bias. By addressing these challenges and responsibly embracing the potential of AI, the medical field may eventually be able to harness its power to improve patient care and outcomes.

Relationships between throwing mechanics and shoulder anterior force in high school and professional baseball pitchers.

Background: Excessive shoulder anterior force has been implicated in pathology of the rotator cuff in little league and professional baseball pitchers; in particular, anterior laxity, posterior stiffness, and glenohumeral joint impingement. Distinctly characterized motions associated with excessive shoulder anterior force remain poorly understood. Methods: High school and professional pitchers were instructed to throw fastballs while being evaluated with 3D motion capture (480 Hz). A supplementary random forest model was designed and implemented to identify the most important features for regressing to shoulder anterior force, with subsequent standardized regression coefficients to quantify directionality. Results: 130 high school pitchers (16.3 ± 1.2 yrs; 179.9 ± 7.7 cm; 74.5 ± 12.0 kg) and 322 professionals (21.9 ± 2.1 yrs; 189.7 ± 5.7 cm; 94.8 ± 9.5 kg) were included. Random forest models determined nearly all the variance for professional pitchers (R2 = 0.96), and less than half for high school pitchers (R2 = 0.41). Important predictors of shoulder anterior force in high school pitchers included: trunk flexion at maximum shoulder external rotation (MER) (X.IncMSE = 2.4, ß = -0.23, p < 0.001), shoulder external rotation at ball release (BR)(X.IncMSE = 1.7, ß = -0.34, p < 0.001), and shoulder abduction at BR (X.IncMSE = 3.1, ß = 0.17, p < 0.001). In professional pitchers, shoulder horizontal adduction at foot contact (FC) was the highest predictor (X.IncMSE = 13.9, ß = 0.50, p < 0.001), followed by shoulder external rotation at FC (X.IncMSE = 3.6, ß = 0.26, p < 0.001), and maximum elbow extension velocity (X.IncMSE = 8.5, ß = 0.19, p < 0.001). Conclusion: A random forest model successfully selected a subset of features that accounted for the majority of variance in shoulder anterior force for professional pitchers; however, less than half of the variance was accounted for in high school pitchers. Temporal and kinematic movements at the shoulder were prominent predictors of shoulder anterior force for both groups. Clinical relevance: : Our statistical model successfully identified a combination of features with the ability to adequately explain the majority of variance in anterior shoulder force among high school and professional pitchers. To minimize shoulder anterior force, high school pitchers should emphasize decreased shoulder abduction at BR, while professionals can decrease shoulder horizontal adduction at FC.

Biomechanical Characteristics of Ulnar Collateral Ligament Injuries Treated With and Without Augmentation: A Network Meta-analysis of Controlled Laboratory Studies.

BACKGROUND: Treatment of ulnar collateral ligament (UCL) tears with suture tape augmentation has gained interest given preliminary reports of favorable biomechanical characteristics. No study to date has quantitatively assessed the biomechanical effects of multiple augmentation techniques relative to the native UCL. PURPOSE: To perform a systematic review and meta-analysis of controlled laboratory studies to assess and comparatively rank biomechanical effects of UCL repair or reconstruction with or without augmentation. STUDY DESIGN: Systematic review and meta-analysis; Level of evidence, 4. METHODS: PubMed, OVID/Medline, and Cochrane databases were queried in January 2023. A frequentist network meta-analytic approach was used to perform mixed-treatment comparisons of UCL repair and reconstruction techniques with and without augmentation, with the native UCL as the reference condition. Pooled treatment estimates were quantified under the random-effects assumption. Competing treatments were ranked in the network meta-analysis by using point estimates and standard errors to calculate P scores (greater P score indicates superiority of treatment for given outcome). RESULTS: Ten studies involving 206 elbow specimens in which a distal UCL tear was simulated were included. UCL reconstruction with suture tape augmentation (AugRecon) restored load to failure to a statistically noninferior magnitude (mean difference [MD], -1.99 N·m; 95% CI, -10.2 to 6.2 N·m; P = .63) compared with the native UCL. UCL reconstruction (Recon) (MD, -12.7 N·m; P < .001) and UCL repair with suture tape augmentation (AugRepair) (MD, -14.8 N·m; P < .001) were both statistically inferior to the native UCL. The AugRecon condition conferred greater load to failure compared with Recon (P < .001) and AugRepair (P = .002) conditions. AugRecon conferred greater torsional stiffness relative to all other conditions and was not statistically different from the native UCL (MD, 0.32 N·m/deg; 95% CI, -0.30 to 0.95 N·m/deg; P = .31). Medial ulnohumeral gapping was not statistically different for the AugRepair (MD, 0.30 mm; 95% CI, -1.22 to 1.82 mm; P = .70), AugRecon (MD, 0.57 mm; 95% CI, -0.70 to 1.84 mm; P = .38), or Recon (MD, 1.02 mm; 95% CI, -0.02 to 2.05 mm; P = .055) conditions compared with the native UCL. P-score analysis indicated that AugRecon was the most effective treatment for increasing ultimate load to failure and torsional stiffness, whereas AugRepair was the most effective for minimizing medial gapping. CONCLUSION: AugRecon restored load to failure and torsional stiffness most similar to the parameters of the native UCL, whereas Recon and AugRepair did not restore the same advantageous properties at time zero. Medial ulnohumeral gapping during a valgus load was minimized by all 3 treatments. Based on network interactions, AugRecon was the superior treatment approach for restoring important biomechanical features of the UCL at time zero that are jeopardized during a complete distal tear.

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.

Low Rates of Postoperative Complications and Revision Surgery After Primary Medial Elbow Ulnar Collateral Ligament Repair.

Purpose: To evaluate the incidence of early postoperative complications and revision surgery in patients who underwent primary medial ulnar collateral ligament (MUCL) repair with minimum of 2-year follow-up. Methods: A retrospective review of a national insurance database was conducted to identify patients with MUCL injuries who underwent primary MUCL repair between 2015 to 2020 with minimum 2-year follow-up. Patients >40 years of age and those who had concomitant elbow fractures or dislocations, lateral UCL injures, medial epicondylitis, elbow arthritis, or a history of previous elbow injury/surgery were excluded. The number of patients who underwent a concomitant ulnar nerve procedure (transposition or decompression) during the primary MUCL repair was recorded. Complications within 90 days of surgery and the incidence and timing of subsequent ipsilateral ulnar nerve surgery or revision MUCL surgery were assessed. Results: A total of 313 patients (63.6% male) were included. The mean age was 20.3 ± 6.9 years, and mean follow-up was 3.7 ± 1.3 years. Concomitant ulnar nerve transposition or decompression was performed in 34.2% (N = 107). The early postoperative complication rate was 7.3% (N = 23). The most common complication was ulnar neuropathy (5.8%, N = 18). Wound complications, elbow stiffness, and medial epicondyle fractures were much less common (N = 5). Sixteen of 18 (88.9%) patients with postoperative ulnar neuropathy underwent transposition or decompression at the time of primary repair. Of these 18 patients, 5 (27.8%) underwent a subsequent ulnar nerve surgery (1 primary and 4 secondary), with the majority occurring within 6 months. The incidence of revision MUCL surgery was low (1.0%, N=3), with all 3 patients undergoing MUCL reconstruction. Conclusion: There was a low incidence of early postoperative complications (7.3%) and 2-year revision MUCL surgery (1.0%) in young patients who underwent primary MUCL repair with no additional ligamentous, fracture, and dislocation-related diagnoses. All 3 (1.0%) MUCL revisions underwent reconstruction. Level of Evidence: Level IV, therapeutic case series.

Variability of the femoral mechanical-anatomical axis angle and its implications in primary and revision total knee arthroplasty.

Aims: Distal femoral resection in conventional total knee arthroplasty (TKA) utilizes an intramedullary guide to determine coronal alignment, commonly planned for 5° of valgus. However, a standard 5° resection angle may contribute to malalignment in patients with variability in the femoral anatomical and mechanical axis angle. The purpose of the study was to leverage deep learning (DL) to measure the femoral mechanical-anatomical axis angle (FMAA) in a heterogeneous cohort. Methods: Patients with full-limb radiographs from the Osteoarthritis Initiative were included. A DL workflow was created to measure the FMAA and validated against human measurements. To reflect potential intramedullary guide placement during manual TKA, two different FMAAs were calculated either using a line approximating the entire diaphyseal shaft, and a line connecting the apex of the femoral intercondylar sulcus to the centre of the diaphysis. The proportion of FMAAs outside a range of 5.0° (SD 2.0°) was calculated for both definitions, and FMAA was compared using univariate analyses across sex, BMI, knee alignment, and femur length. Results: The algorithm measured 1,078 radiographs at a rate of 12.6 s/image (2,156 unique measurements in 3.8 hours). There was no significant difference or bias between reader and algorithm measurements for the FMAA (p = 0.130 to 0.563). The FMAA was 6.3° (SD 1.0°; 25% outside range of 5.0° (SD 2.0°)) using definition one and 4.6° (SD 1.3°; 13% outside range of 5.0° (SD 2.0°)) using definition two. Differences between males and females were observed using definition two (males more valgus; p < 0.001). Conclusion: We developed a rapid and accurate DL tool to quantify the FMAA. Considerable variation with different measurement approaches for the FMAA supports that patient-specific anatomy and surgeon-dependent technique must be accounted for when correcting for the FMAA using an intramedullary guide. The angle between the mechanical and anatomical axes of the femur fell outside the range of 5.0° (SD 2.0°) for nearly a quarter of patients.

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.

High Rate of Clinically Meaningful Achievement in Outcomes After Subacromial Balloon Spacer Implantation for Massive Irreparable Rotator Cuff Tears: A Systematic Review and Meta-analysis.

BACKGROUND: Subacromial balloon spacers have been introduced as a potential treatment option for patients with massive irreparable rotator cuff tears. However, it is important to comprehensively assess the clinical efficacy of this procedure in the context of an increasing amount of contemporary literature. PURPOSE: To perform a systematic review of the contemporary literature to understand the propensity for clinically meaningful improvements after subacromial balloon spacer implantation for massive irreparable rotator cuff tears. STUDY DESIGN: Systematic review and meta-analysis; Level of evidence, 4. METHODS: The PubMed, Ovid/MEDLINE, and Cochrane databases were queried in July 2022 for data pertaining to studies reporting clinically significant outcomes after subacromial balloon spacer implantation. Freeman-Tukey double arcsine transformation was used to quantify the pooled rate of clinically meaningful improvements in outcomes as evaluated using the minimal clinically important difference (MCID), Patient Acceptable Symptom State (PASS), and substantial clinical benefit (SCB). Qualitative analysis was performed when data were variably presented to avoid misleading reporting. RESULTS: There were 10 studies included, all of which reported MCID achievement. The overall pooled rate of MCID achievement for the Constant-Murley score was 83% (95% CI, 71%-93%; range, 40%-98%), with 6 of 8 studies reporting rates equal to or exceeding 85%. One study reported a 98% rate of PASS achievement for the Constant-Murley score at 3-year follow-up. The rate of MCID achievement for the American Shoulder and Elbow Surgeons (ASES) score ranged between 83% and 87.5%. The rate of PASS achievement for the ASES score was 56% at 2-year follow-up, while the rate of SCB achievement for the ASES score was 83% and 82% at 1- and 2-year follow-up, respectively. At 1-year follow-up, 74% and 78% of patients achieved the MCID for the Numeric Rating Scale and Oxford Shoulder Score, respectively. At 3 years, 69% of patients achieved the MCID for the Numeric Rating Scale and 87% achieved it for the Oxford Shoulder Score. CONCLUSION: Patients who underwent isolated subacromial balloon spacer implantation for massive irreparable rotator cuff tears demonstrated a high rate of clinically significant improvement in outcomes at short- to mid-term follow-up. A paucity of literature exists to appropriately define and evaluate the rates of achieving the PASS and SCB after subacromial balloon spacer implantation, necessitating further study.

30-day to 10-year mortality rates following total hip arthroplasty: a meta-analysis of the last decade (2011-2021).

BACKGROUND: Mortality after total hip arthroplasty (THA) is a rare but devastating complication. This meta-analysis aimed to: (1) determine the mortality rates at 30 days, 90 days, 1 year, 5 years and 10 years after THA; (2) identify risk factors and causes of mortality after THA. METHODS: Pubmed, MEDLINE, Cochrane, EBSCO Host, and Google Scholar databases were queried for studies reporting mortality rates after primary elective, unilateral THA. Inverse-proportion models were constructed to quantify the incidence of all-cause mortality at 30 days, 90 days, 1 year, 5 years and 10 years after THA. Random-effects multiple regression was performed to investigate the potential effect modifiers of age (at time of THA), body mass index, and gender. RESULTS: A total of 53 studies (3,297,363 patients) were included. The overall mortality rate was 3.9%. The 30-day mortality was 0.49% (95% CI; 0.23-0.84). Mortality at 90 days was 0.47% (95% CI, 0.38-0.57). Mortality increased exponentially between 90 days and 5 years, with a 1-year mortality rate of 1.90% (95% CI, 1.22-2.73) and a 5-year mortality rate of 9.85% (95% CI, 5.53-15.22). At 10-year follow-up, the mortality rate was 16.43% (95% CI, 1.17-22.48). Increasing comorbidity indices, socioeconomic disadvantage, age, anaemia, and smoking were found to be risk factors for mortality. The most commonly reported causes of death were ischaemic heart disease, malignancy, and pulmonary disease. CONCLUSIONS: All-cause mortality remains low after contemporary THA. However, 1 out of 10 patients and 1 out of 6 patients were deceased after 5 years and 10 years of THA, respectively. As expected, age, but not BMI or gender, was significantly associated with mortality.

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.

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.

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.

Kinematic and Kinetic Comparisons of Arm Slot Position Between High School and Professional Pitchers.

Background: Throwing arm kinetics differ in pitchers at varying arm slot (AS) positions (frontal-plane arm position at ball release relative to the vertical axis). Purpose: To determine how kinematic and kinetic values differ between professional and high school pitchers with varying AS positions, and whether these differences are similarly observed in both populations. Methods: High school (n = 130) and professional (n = 288) pitchers threw 8 to 12 fastballs under 3-dimensional motion capture technology. Pitchers in each cohort were subdivided based on mean AS position at ball release: AS1 (least degree of AS: most overhand throwing styles), AS2 (intermediate degree of AS: three-quarter throwing styles), or AS3 (greatest degree of AS: most sidearm throwing styles). Kinetic and kinematic parameters were compared between groups. Study Design: Controlled laboratory study. Results: High school pitchers had a more overhand AS at ball release (50° ± 11°) compared with professional pitchers (58° ± 14°) (P < .001). In both cohorts, AS1 pitchers had significantly greater shoulder abduction (high school, P <0.001; professional, P <0.0001) and lateral trunk flexion (high school, P < 0.001; professional, P <0.0001) at ball release compared with AS3 pitchers. Professional pitchers with an AS3 position had significantly delayed timing of maximum upper trunk angular velocity compared with AS1 pitchers (64% ± 7% vs 57% ± 7% of pitch time, respectively; P < .0001). A significant positive correlation between AS and elbow flexion torque was found in high school pitchers (P = .002; ß = 0.28), and a significant negative correlation between AS and elbow varus torque (P < .001; ß = -0.22) and shoulder internal rotation torque (P < .001; ß = -0.20) was noted in professional pitchers. Conclusion: AS position was related to shoulder abduction and trunk lateral tilt. Professional and high school pitchers with varying AS positions did not experience similar changes in throwing arm kinetics. Clinical Relevance: In professional pitchers, the earlier onset of maximum upper trunk angular velocity with overhand throwing style may reflect inappropriate pelvis-trunk timing separation, a parameter implicated in upper extremity injury, and the negative correlation between AS and elbow varus and shoulder internal rotation torque suggests that both excessive and minimal AS positions have negative implications.

Risk Factors for Graft Failure After Meniscal Allograft Transplantation: A Systematic Review and Meta-analysis.

Background: Graft failure after meniscal allograft transplantation (MAT) may necessitate revision surgery or conversion to arthroplasty. A comprehensive understanding of the risk factors for failure after MAT of the knee may facilitate more informed shared decision-making discussions before surgery and help determine whether MAT should be performed based on patient risk. Purpose: To perform a systematic review and meta-analysis of risk factors associated with graft failure after MAT of the knee. Study Design: Systematic review; Level of evidence, 4. Methods: The PubMed, OVID/Medline, and Cochrane databases were queried in October 2021. Data pertaining to study characteristics and risk factors associated with failure after MAT were recorded. DerSimonian-Laird binary random-effects models were constructed to quantitatively evaluate the association between risk factors and MAT graft failure by generating effect estimates in the form of odds ratios (ORs) with 95% CIs. Qualitative analysis was performed to describe risk factors that were variably reported. Results: In total, 17 studies including 2184 patients were included. The overall pooled prevalence of failure at the latest follow-up was 17.8% (range, 3.3%-81.0%). In 10 studies reporting 5-year failure rates, the pooled prevalence of failure was 10.9% (range, 4.7%-23%). In 4 studies reporting 10-year failure rates, the pooled prevalence was 22.7% (range, 8.1%-55.0%). A total of 39 risk factors were identified, although raw data presented in a manner amenable to meta-analysis only allowed for 3 to be explored quantitatively. There was strong evidence to support that an International Cartilage Regeneration & Joint Preservation Society grade >3a (OR, 5.32; 95% CI, 2.75-10.31; P < .001) was a significant risk factor for failure after MAT. There was no statistically significant evidence to incontrovertibly support that patient sex (OR, 2.16; 95% CI, 0.83-5.64; P = .12) or MAT laterality (OR, 1.11; 95% CI, 0.38-3.28; P = .85) was associated with increased risk of failure after MAT. Conclusion: Based on the studies reviewed, there was strong evidence to suggest that degree of cartilage damage at the time of MAT is associated with graft failure; however, the evidence was inconclusive on whether laterality or patient sex is associated with graft failure.

A clinical prediction model for 10-year risk of self-reported osteoporosis diagnosis in pre- and perimenopausal women.

A machine learning model using clinical, laboratory, and imaging data was developed to predict 10-year risk of menopause-related osteoporosis. The resulting predictions, which are sensitive and specific, highlight distinct clinical risk profiles that can be used to identify patients most likely to be diagnosed with osteoporosis. PURPOSE: The aim of this study was to incorporate demographic, metabolic, and imaging risk factors into a model for long-term prediction of self-reported osteoporosis diagnosis. METHODS: This was a secondary analysis of 1685 patients from the longitudinal Study of Women's Health Across the Nation using data collected between 1996 and 2008. Participants were pre- or perimenopausal women between 42 and 52 years of age. A machine learning model was trained using 14 baseline risk factors-age, height, weight, body mass index, waist circumference, race, menopausal status, maternal osteoporosis history, maternal spine fracture history, serum estradiol level, serum dehydroepiandrosterone level, serum thyroid-stimulating hormone level, total spine bone mineral density, and total hip bone mineral density. The self-reported outcome was whether a doctor or other provider had told participants they have osteoporosis or treated them for osteoporosis. RESULTS: At 10-year follow-up, a clinical osteoporosis diagnosis was reported by 113 (6.7%) women. Area under the receiver operating characteristic curve of the model was 0.83 (95% confidence interval, 0.73-0.91) and Brier score was 0.054 (95% confidence interval, 0.035-0.074). Total spine bone mineral density, total hip bone mineral density, and age had the largest contributions to predicted risk. Using two discrimination thresholds, stratification into low, medium, and high risk, respectively, was associated with likelihood ratios of 0.23, 3.2, and 6.8. At the lower threshold, sensitivity was 0.81, and specificity was 0.82. CONCLUSION: The model developed in this analysis integrates clinical data, serum biomarker levels, and bone mineral densities to predict 10-year risk of osteoporosis with good performance.