Segond Fractures Can Be Identified With Excellent Accuracy Utilizing Deep Learning on Anteroposterior Knee Radiographs.

Purpose: To develop a deep learning model for the detection of Segond fractures on anteroposterior (AP) knee radiographs and to compare model performance to that of trained human experts. Methods: AP knee radiographs were retrieved from the Hospital for Special Surgery ACL Registry, which enrolled patients between 2009 and 2013. All images corresponded to patients who underwent anterior cruciate ligament reconstruction by 1 of 23 surgeons included in the registry data. Images were categorized into 1 of 2 classes based on radiographic evidence of a Segond fracture and manually annotated. Seventy percent of the images were used to populate the training set, while 20% and 10% were reserved for the validation and test sets, respectively. Images from the test set were used to compare model performance to that of expert human observers, including an orthopaedic surgery sports medicine fellow and a fellowship-trained orthopaedic sports medicine surgeon with over 10 years of experience. Results: A total of 324 AP knee radiographs were retrieved, of which 34 (10.4%) images demonstrated evidence of a Segond fracture. The overall mean average precision (mAP) was 0.985, and this was maintained on the Segond fracture class (mAP = 0.978, precision = 0.844, recall = 1). The model demonstrated 100% accuracy with perfect sensitivity and specificity when applied to the independent testing set and the ability to meet or exceed human sensitivity and specificity in all cases. Compared to an orthopaedic surgery sports medicine fellow, the model required 0.3% of the total time needed to evaluate and classify images in the independent test set. Conclusions: A deep learning model was developed and internally validated for Segond fracture detection on AP radiographs and demonstrated perfect accuracy, sensitivity, and specificity on a small test set of radiographs with and without Segond fractures. The model demonstrated superior performance compared with expert human observers. Clinical Relevance: Deep learning can be used for automated Segond fracture identification on radiographs, leading to improved diagnosis of easily missed concomitant injuries, including lateral meniscus tears. Automated identification of Segond fractures can also enable large-scale studies on the incidence and clinical significance of these fractures, which may lead to improved management and outcomes for patients with knee injuries.

Clinical Results of Primary Repair Versus Reconstruction of the Anterior Cruciate Ligament: A Systematic Review and Meta-analysis of Contemporary Trials.

Background: Primary anterior cruciate ligament (ACL) repair has gained renewed interest in select centers for patients with proximal or midsubstance ACL tears. Therefore, it is important to reassess contemporary clinical outcomes of ACL repair to determine whether a clinical benefit exists over the gold standard of ACL reconstruction (ACLR). Purpose: To (1) perform a meta-analysis of comparative trials to determine whether differences in clinical outcomes and adverse events exist between ACL repair versus ACLR and (2) synthesize the midterm outcomes of available trials. Study Design: Systematic review; Level of evidence, 3. Methods: The PubMed, OVID/Medline, and Cochrane databases were queried in August 2023 for prospective and retrospective clinical trials comparing ACL repair and ACLR. Data pertaining to tear location, surgical technique, adverse events, and clinical outcome measures were recorded. DerSimonian-Laird random-effects models were constructed to quantitatively evaluate the association between ACL repair/ACLR, adverse events, and clinical outcomes. A subanalysis of minimum 5-year outcomes was performed. Results: Twelve studies (893 patients; 464 ACLR and 429 ACL repair) were included. Random-effects models demonstrated a higher relative risk (RR) of recurrent instability/clinical failure (RR = 1.64; 95% confidence interval [CI], 1.04-2.57; P = .032), revision ACLR (RR = 1.63; 95% CI, 1.03-2.59; P = .039), and hardware removal (RR = 4.94; 95% CI, 2.10-11.61; P = .0003) in patients who underwent primary ACL repair versus ACLR. The RR of reoperations and complications (knee-related) were not significantly different between groups. No significant differences were observed when comparing patient-reported outcome scores. In studies with minimum 5-year outcomes, no significant differences in adverse events or Lysholm scores were observed. Conclusion: In contemporary comparative trials of ACL repair versus ACLR, the RR of clinical failure, revision surgery due to ACL rerupture, and hardware removal was greater for primary ACL repair compared with ACLR. There were no observed differences in patient-reported outcome scores, reoperations, or knee-related complications between approaches. In the limited literature reporting on minimum 5-year outcomes, significant differences in adverse events or the International Knee Documentation Committee score were not observed.

Platelet-Rich Plasma Versus Alternative Injections for Osteoarthritis of the Knee: A Systematic Review and Statistical Fragility Index-Based Meta-analysis of Randomized Controlled Trials.

BACKGROUND: Based in part on the results of randomized controlled trials (RCTs) that suggest a beneficial effect over alternative treatment options, the use of platelet-rich plasma (PRP) for the management of knee osteoarthritis (OA) is widespread and increasing. However, the extent to which these studies are vulnerable to slight variations in the outcomes of patients remains unknown. PURPOSE: To evaluate the statistical fragility of conclusions from RCTs that reported outcomes of patients with knee OA who were treated with PRP versus alternative nonoperative management strategies. STUDY DESIGN: Systematic review and meta-analysis; Level of evidence, 2. METHODS: All RCTs comparing PRP with alternative nonoperative treatment options for knee OA were identified. The fragility index (FI) and reverse FI were applied to assess the robustness of conclusions regarding the efficacy of PRP for knee OA. Meta-analyses were performed to determine the minimum number of patients from ≥1 trials included in the meta-analysis for which a modification on the event status would change the statistical significance of the pooled treatment effect. RESULTS: In total, this analysis included outcomes from 1993 patients with a mean ± SD age of 58.0 ± 3.8 years. The mean number of events required to reverse significance of individual RCTs (FI) was 4.57 ± 5.85. Based on random-effects meta-analyses, PRP demonstrated a significantly higher rate of successful outcomes when compared with hyaluronic acid (P = .002; odds ratio [OR], 2.19; 95% CI, 1.33-3.62), as well as higher rates of patient-reported symptom relief (P = .019; OR, 1.55; 95% CI, 1.07-2.24), not requiring a reintervention after the initial injection treatment (P = .002; OR, 2.17; 95% CI, 1.33-3.53), and achieving the minimal clinically important difference (MCID) for pain improvement (P = .007; OR, 6.19; 95% CI, 1.63-23.42) when compared with all alternative nonoperative treatments. Overall, the mean number of events per meta-analysis required to change the statistical significance of the pooled treatment effect was 8.67 ± 4.50. CONCLUSION: Conclusions drawn from individual RCTs evaluating PRP for knee OA demonstrated slight robustness. On meta-analysis, PRP demonstrated a significant advantage over hyaluronic acid as well as improved symptom relief, lower rates of reintervention, and more frequent achievement of the MCID for pain improvement when compared with alternative nonoperative treatment options. Statistically significant pooled treatment effects evaluating PRP for knee OA are more robust than approximately half of all comparable meta-analyses in medicine and health care. Future RCTs and meta-analyses should consider reporting FIs and fragility quotients to facilitate interpretation of results in their proper context.

Differences in total shoulder arthroplasty utilization and 30-day outcomes among White, Black, and Hispanic patients: do disparities exist in the outpatient setting?

BACKGROUND: In the United States, efforts to improve efficiency and reduce healthcare costs are shifting more total shoulder arthroplasty (TSA) surgeries to the outpatient setting. However, whether racial and ethnic disparities in access to high-quality outpatient TSA care exist remains to be elucidated. The purpose of this study was to assess racial/ethnic differences in relative outpatient TSA utilization and perioperative outcomes using a large national surgical database. METHODS: White, Black, and Hispanic patients who underwent TSA between 2017 and 2021 were identified from the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) database. Baseline demographic and clinical characteristics were collected, and rates of outpatient utilization, adverse events, readmission, reoperation, nonhome discharge, and mortality within 30 days of surgery were compared between racial/ethnic groups. Race/ethnicity-specific trends in utilization of outpatient TSA were assessed, and multivariable logistic regression was used to adjust for baseline demographic factors and comorbidities. RESULTS: A total of 21,186 patients were included, consisting of 19,135 (90.3%) White, 1093 (5.2%) Black, and 958 (4.5%) Hispanic patients and representing 17,649 (83.3%) inpatient and 3537 (16.7%) outpatient procedures. Black and Hispanic patients were generally younger and less healthy than White patients, yet incidences of complications, nonhome discharge, readmission, reoperation, and death within 30 days were similar across groups following outpatient TSA (P > .050 for all). Relative utilization of outpatient TSA increased by 28.7% among White patients, 29.5% among Black patients, and 38.6% among Hispanic patients (ptrend<0.001 for all). Hispanic patients were 64% more likely than White patients to undergo TSA as an outpatient procedure across the study period (OR: 1.64, 95% CI 1.40-1.92, P < .001), whereas odds did not differ between Black and White patients (OR: 1.04, 95% CI 0.87-1.23, P = .673). CONCLUSION: Relative utilization of outpatient TSA remains highest among Hispanic patients but has been significantly increasing across all racial and ethnic groups, now accounting for more than one-third of all TSA procedures. Considering outpatient TSA is associated with fewer complications and lower costs, increasing utilization may represent a promising avenue for reducing disparities in orthopedic shoulder surgery.

National Patterns in Utilization of Knee and Hip Arthroscopy: An Analysis of Racial, Ethnic, and Geographic Disparities in the United States.

Background: Racial and ethnic disparities in the field of orthopaedic surgery have been reported extensively across many subspecialties. However, these data remain relatively sparse in orthopaedic sports medicine, especially with respect to commonly performed procedures including knee and hip arthroscopy. Purpose: To assess (1) differences in utilization of knee and hip arthroscopy between White, Black, Hispanic, and Asian or Pacific Islander patients in the United States (US) and (2) how these differences vary by geographical region. Study Design: Descriptive epidemiology study. Methods: The study sample was acquired from the 2019 National Ambulatory Surgery Sample database. Racial and ethnic differences in age-standardized utilization rates of hip and knee arthroscopy were calculated using survey weights and population estimates from US census data. Poisson regression was used to model age-standardized utilization rates for hip and knee arthroscopy while controlling for several demographic and clinical variables. Results: During the study period, rates of knee arthroscopy utilization among White patients were significantly higher than those of Black, Hispanic, and Asian or Pacific Islander patients (ie, per 100,000, White: 180.5, Black: 113.2, Hispanic: 122.2, and Asian: 58.6). Disparities were even more pronounced among patients undergoing hip arthroscopy, with White patients receiving the procedure at almost 4 to 5 times higher rates (ie, per 100,000, White: 12.6, Black: 3.2, Hispanic: 2.3, Asian or Pacific Islander: 1.8). Disparities in knee and hip arthroscopy utilization between White and non-White patients varied significantly by region, with gaps in knee arthroscopy being most pronounced in the Midwest (adjusted rate ratio, 2.0 [95% CI, 1.9-2.1]) and those in hip arthroscopy being greatest in the West (adjusted rate ratio, 5.3 [95% CI, 4.9-5.6]). Conclusion: Racial and ethnic disparities in the use of knee and hip arthroscopy were found across the US, with decreased rates among Black, Hispanic, and Asian or Pacific Islander patients compared with White patients. Disparities were most pronounced in the Midwest and South and greater for hip than knee arthroscopy, possibly demonstrating emerging inequality in a rapidly growing and evolving procedure across the country.

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.

Challenges and Opportunities for the Use of Patient-Reported Outcome Measures in Orthopaedic Pediatric and Sports Medicine Surgery.

Patient-reported outcome measures (PROMs) are essential tools in assessing treatment response, informing clinical decision making, driving healthcare policy, and providing important prognostic data regarding patient health status change. These tools become essential in orthopaedic disciplines, such as pediatrics and sports medicine, given the diversity of patient populations and procedures. However, the creation and routine administration of standard PROMs alone do not suffice to appropriately facilitate the aforementioned functions. Indeed, both the interpretation and optimal application of PROMs are essential to provide to achieve greatest clinical benefit. Contemporary developments and technologies surrounding PROMs may help augment this benefit, including the application of artificial intelligence, novel PROM structure with improved interpretability and validity, and PROM delivery methods that provide increased access to patients resulting in greater compliance and data acquisition yields. Despite these exciting innovations, several challenges remain in this realm that must be addressed to continue to advance the clinical usefulness and subsequent benefit of PROMs. This review will highlight the opportunities and challenges surrounding contemporary PROM use in the orthopaedic subspecialties of pediatrics and sports medicine.

Quadriceps Weakness is Associated with Neuroplastic Changes Within Specific Corticospinal Pathways and Brain Areas After Anterior Cruciate Ligament Reconstruction: Theoretical Utility of Motor Imagery-Based Brain-Computer Interface Technology for Rehabilitation.

Persistent quadriceps weakness is a problematic sequela of anterior cruciate ligament reconstruction (ACLR). The purposes of this review are to summarize neuroplastic changes after ACL reconstruction; provide an overview of a promising interventions, motor imagery (MI), and its utility in muscle activation; and propose a framework using a brain-computer interface (BCI) to augment quadriceps activation. A literature review of neuroplastic changes, MI training, and BCI-MI technology in postoperative neuromuscular rehabilitation was conducted in PubMed, Embase, and Scopus. Combinations of the following search terms were used to identify articles: "quadriceps muscle," "neurofeedback," "biofeedback," "muscle activation," "motor learning," "anterior cruciate ligament," and "cortical plasticity." We found that ACLR disrupts sensory input from the quadriceps, which results in reduced sensitivity to electrochemical neuronal signals, an increase in central inhibition of neurons regulating quadriceps control and dampening of reflexive motor activity. MI training consists of visualizing an action, without physically engaging in muscle activity. Imagined motor output during MI training increases the sensitivity and conductivity of corticospinal tracts emerging from the primary motor cortex, which helps "exercise" the connections between the brain and target muscle tissues. Motor rehabilitation studies using BCI-MI technology have demonstrated increased excitability of the motor cortex, corticospinal tract, spinal motor neurons, and disinhibition of inhibitory interneurons. This technology has been validated and successfully applied in the recovery of atrophied neuromuscular pathways in stroke patients but has yet to be investigated in peripheral neuromuscular insults, such as ACL injury and reconstruction. Well-designed clinical studies may assess the impact of BCI on clinical outcomes and recovery time. Quadriceps weakness is associated with neuroplastic changes within specific corticospinal pathways and brain areas. BCI-MI shows strong potential for facilitating recovery of atrophied neuromuscular pathways after ACLR and may offer an innovative, multidisciplinary approach to orthopaedic care. Level of Evidence: V, expert opinion.

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.

MCID and PASS in Knee Surgeries. Theoretical Aspects and Clinical Relevance References.

The application and interpretation of patient-reported outcome measures (PROM), following knee injuries, pathologies, and interventions, can be challenging. In recent years, the literature has been enriched with metrics to facilitate our understanding and interpretation of these outcome measures. Two commonly utilized tools include the minimal clinically important difference (MCID) and the patient acceptable symptoms state (PASS). These measures have demonstrated clinical value, however, they have often been under- or mis-reported. It is paramount to use them to understand the clinical significance of any statistically significant results. Still, it remains important to know their caveats and limitations. In this focused report on MCID and PASS, their definitions, methods of calculations, clinical relevance, interpretations, and limitations are reviewed and presented in a simple approach.

Evaluating the Need for Preoperative MRI Before Primary Hip Arthroscopy in Patients 40 Years and Younger With Femoroacetabular Impingement Syndrome: A Multicenter Comparative Analysis.

Background: Routine hip magnetic resonance imaging (MRI) before arthroscopy for patients with femoroacetabular impingement syndrome (FAIS) offers questionable clinical benefit, delays surgery, and wastes resources. Purpose: To assess the clinical utility of preoperative hip MRI for patients aged ≤40 years who were undergoing primary hip arthroscopy and who had a history, physical examination findings, and radiographs concordant with FAIS. Study Design: Cohort study; Level of evidence, 3. Methods: Included were 1391 patients (mean age, 25.8 years; 63% female; mean body mass index, 25.6) who underwent hip arthroscopy between August 2015 and December 2021 by 1 of 4 fellowship-trained hip surgeons from 4 referral centers. Inclusion criteria were FAIS, primary surgery, and age ≤40 years. Exclusion criteria were MRI contraindication, reattempt of nonoperative management, and concomitant periacetabular osteotomy. Patients were stratified into those who were evaluated with preoperative MRI versus those without MRI. Those without MRI received an MRI before surgery without deviation from the established surgical plan. All preoperative MRI scans were compared with the office evaluation and intraoperative findings to assess agreement. Time from office to arthroscopy and/or MRI was recorded. MRI costs were calculated. Results: Of the study patients, 322 were not evaluated with MRI and 1069 were. MRI did not alter surgical or interoperative plans. Both groups had MRI findings demonstrating anterosuperior labral tears treated intraoperatively (99.8% repair, 0.2% debridement, and 0% reconstruction). Compared with patients who were evaluated with MRI and waited 63.0 ± 34.6 days, patients who were not evaluated with MRI underwent surgery 6.5 ± 18.7 days after preoperative MRI. MRI delayed surgery by 24.0 ± 5.3 days and cost a mean $2262 per patient. Conclusion: Preoperative MRI did not alter indications for primary hip arthroscopy in patients aged ≤40 years with a history, physical examination findings, and radiographs concordant with FAIS. Rather, MRI delayed surgery and wasted resources. Routine hip MRI acquisition for the younger population with primary FAIS with a typical presentation should be challenged.

Risk Factors for Failure After Osteochondral Allograft Transplantation of the Knee: A Systematic Review and Exploratory Meta-analysis.

BACKGROUND: Graft failure after osteochondral allograft transplantation (OCA) of the knee is a devastating outcome, often necessitating subsequent interventions. A comprehensive understanding of the risk factors for failure after OCA of the knee may provide enhanced prognostic data for the knee surgeon and facilitate more informed shared decision-making discussions before surgery. PURPOSE: To perform a systematic review and meta-analysis of risk factors associated with graft failure after OCA of the knee. STUDY DESIGN: Systematic review and meta-analysis; Level of evidence, 4. METHODS: The PubMed, Ovid/MEDLINE, and Cochrane databases were queried in April 2021. Data pertaining to study characteristics and risk factors associated with failure after OCA were recorded. DerSimonian-Laird binary random-effects models were constructed to quantitatively evaluate the association between risk factors and graft failure by generating effect estimates in the form of odds ratios (ORs) with 95% CIs, while mean differences (MDs) were calculated for continuous data. Qualitative analysis was performed to describe risk factors that were variably reported. RESULTS: A total of 16 studies consisting of 1401 patients were included. The overall pooled prevalence of failure was 18.9% (range, 10%-46%). There were 44 risk factors identified, of which 9 were explored quantitatively. There was strong evidence to support that the presence of bipolar chondral defects (OR, 4.20 [95% CI, 1.17-15.08]; P = .028) and male sex (OR, 2.04 [95% CI, 1.17-3.55]; P = .012) were significant risk factors for failure after OCA. Older age (MD, 5.06 years [95% CI, 1.44-8.70]; P = .006) and greater body mass index (MD, 1.75 kg/m2 [95% CI, 0.48-3.03]; P = .007) at the time of surgery were also significant risk factors for failure after OCA. There was no statistically significant evidence to incontrovertibly support that concomitant procedures, chondral defect size, and defect location were associated with an increased risk of failure after OCA. CONCLUSION: Bipolar chondral defects, male sex, older age, and greater body mass index were significantly associated with an increased failure rate after OCA of the knee. No statistically significant evidence presently exists to support that chondral defect size and location or concomitant procedures are associated with an increased graft failure rate after OCA of the knee. Additional studies are needed to evaluate these associations.

Multivariable regression: understanding one of medicine's most fundamental statistical tools.

Multivariable regression is a fundamental tool that drives observational research in orthopaedic surgery. However, regression analyses are not always implemented correctly. This study presents a basic overview of regression analyses and reviews frequent points of confusion. Topics include linear, logistic, and time-to-event regressions, causal inference, confounders, overfitting, missing data, multicollinearity, interactions, and key differences between multivariable versus multivariate regression. The goal is to provide clarity regarding the use and interpretation of multivariable analyses for those attempting to increase their statistical literacy in orthopaedic research.

Natural language processing: using artificial intelligence to understand human language in orthopedics.

Natural language processing (NLP) describes the broad field of artificial intelligence by which computers are trained to understand and generate human language. Within healthcare research, NLP is commonly used for variable extraction and classification/cohort identification tasks. While these tools are becoming increasingly popular and available as both open-source and commercial products, there is a paucity of the literature within the orthopedic space describing the key tasks within these powerful pipelines. Curation and navigation of the electronic medical record are becoming increasingly onerous, and it is important for physicians and other healthcare professionals to understand potential methods of harnessing this large data resource. The purpose of this study is to provide an overview of the tasks required to develop an NLP pipeline for orthopedic research and present recent examples of successful implementations.

Hip Arthroscopy With Bone Marrow Aspirate Injection for Patients With Symptomatic Labral Tears and Early Degenerative Changes Shows Similar Improvement Compared With Patients Undergoing Hip Arthroscopy With Symptomatic Labral Tears Without Arthritis.

PURPOSE: To define the clinical effect of intra-articular injection of iliac crest-derived bone marrow aspirate concentrate (BMAC) at the time of hip arthroscopy in patients with symptomatic labral tears and early radiographic degenerative changes. METHODS: A retrospective review of a prospectively collected hip registry database was performed. Patients with symptomatic labral tears and Tönnis grade 1 or 2 degenerative changes who underwent labrum-preserving hip arthroscopy with BMAC injection were included and were matched with patients who underwent hip arthroscopy without BMAC injection. Patient-reported outcomes (PROs) collected preoperatively and up to 2 years postoperatively included the modified Harris Hip Score, Hip Outcome Score-Activities of Daily Living, Hip Outcome Score-Sport, and International Hip Outcome Tool 33 score. Clinical relevance was measured with the minimal clinically important difference, patient acceptable symptom state, and substantial clinical benefit for each outcome score. RESULTS: A total of 35 patients underwent labrum-preserving hip arthroscopy with BMAC injection and were matched with 35 control patients. There were no differences in demographic characteristics between the groups (P > .05). The BMAC group consisted of 22 patients (62.9%) with Tönnis grade 1 changes and 13 (37.1%) with Tönnis grade 2 changes, whereas all 35 control patients had Tönnis grade 0 hips. All PROs were significantly improved in both groups at 2 years, with no difference in improvement. The rate of failure requiring conversion to total hip arthroplasty was 14.3% (mean, 1.6 years postoperatively) in the BMAC group and 5.7% (mean, 7 years postoperatively) in the control group (P = .09). The difference in the frequency of patients achieving the minimal clinically important difference, patient acceptable symptom state, and substantial clinical benefit was not statistically significant between cohorts. CONCLUSIONS: In a challenging group of patients with symptomatic labral tears and early radiographic degenerative changes, hip arthroscopy with BMAC injection results in statistically and clinically significant improvement in PROs comparable to a group of patients with nonarthritic hips undergoing hip arthroscopy at short-term follow-up. LEVEL OF EVIDENCE: Level III, retrospective comparative therapeutic trial.

Unsupervised machine learning methods and emerging applications in healthcare.

Unsupervised machine learning methods are important analytical tools that can facilitate the analysis and interpretation of high-dimensional data. Unsupervised machine learning methods identify latent patterns and hidden structures in high-dimensional data and can help simplify complex datasets. This article provides an overview of key unsupervised machine learning techniques including K-means clustering, hierarchical clustering, principal component analysis, and factor analysis. With a deeper understanding of these analytical tools, unsupervised machine learning methods can be incorporated into health sciences research to identify novel risk factors, improve prevention strategies, and facilitate delivery of personalized therapies and targeted patient care.Level of evidence: I.

A practical guide to the development and deployment of deep learning models for the Orthopedic surgeon: part I.

Deep learning has a profound impact on daily life. As Orthopedics makes use of this rapid escalation in technology, Orthopedic surgeons will need to take leadership roles on deep learning projects. Moreover, surgeons must possess an understanding of what is necessary to design and implement deep learning-based project pipelines. This review provides a practical guide for the Orthopedic surgeon to understand the steps needed to design, develop, and deploy a deep learning pipeline for clinical applications. A detailed description of the processes involved in defining the problem, building the team, acquiring and curating the data, labeling the data, establishing the ground truth, pre-processing and augmenting the data, and selecting the required hardware is provided. In addition, an overview of unique considerations involved in the training and evaluation of deep learning models is provided. This review strives to provide surgeons with the groundwork needed to identify gaps in the clinical landscape that deep learning models may be able to fill and equips them with the knowledge needed to lead an interdisciplinary team through the process of creating novel deep-learning-based solutions to fill those gaps.

Supervised machine learning and associated algorithms: applications in orthopedic surgery.

Supervised learning is the most common form of machine learning utilized in medical research. It is used to predict outcomes of interest or classify positive and/or negative cases with a known ground truth. Supervised learning describes a spectrum of techniques, ranging from traditional regression modeling to more complex tree boosting, which are becoming increasingly prevalent as the focus on "big data" develops. While these tools are becoming increasingly popular and powerful, there is a paucity of literature available that describe the strengths and limitations of these different modeling techniques. Typically, there is no formal training for health care professionals in the use of machine learning models. As machine learning applications throughout medicine increase, it is important that physicians and other health care professionals better understand the processes underlying application of these techniques. The purpose of this study is to provide an overview of commonly used supervised learning techniques with recent case examples within the orthopedic literature. An additional goal is to address disparities in the understanding of these methods to improve communication within and between research teams.

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.