Are our actions matching our words? A review of trainee ethnic and gender diversity in orthopaedic surgery.

Background: There is a lack of physician ethnic and gender diversity amongst surgical specialties. This study analyzes the literature that promotes diversity amongst surgical trainees. Specifically, this study sought to answer (i) how the number of publications regarding diversity in orthopaedic surgery compares to other surgical specialties, (ii) how the number of publications amongst all surgical subspecialties trends over time and (iii) which specific topics regarding diversity are discussed in the surgical literature. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used to query articles from PubMed, Web of Science, Embase and the Cumulative Index to Nursing and Allied Health Literature. Broad inclusion criteria for both ethnic and gender diversity of any surgical specialty were utilized. Results: Our query resulted 1429 publications, of which 408 duplicates were removed, and 701 were excluded on title and abstract screening, leaving 320 to be included. The highest number of related publications was in orthopaedic surgery (n = 73) followed by general surgery (n = 56). Out of 320 total articles, 260 (81.3 %) were published after 2015, and 56 of 73 (76.7 %) orthopaedic-specific articles were published after 2015. Conclusion: Orthopaedic surgery published the most about ethnic and gender diversity, however, still remains one of the least diverse surgical specialties. With the recent increase in publications on diversity in surgical training, close attention should be paid to ethnic and gender diversity amongst surgical trainees over the coming years. Should diversity remain stagnant, diversification efforts may need to be restructured to achieve a diverse surgeon workforce. Key message: Orthopaedic surgery is the surgical subspecialty that publishes the most about trainee ethnic and gender diversity followed by general surgery. With most of this literature being published over the last eight years, it is imperative to pay close attention to the ethnic and gender landscape of the surgeon workforce over the coming years.

Rotator cuff tears.

Rotator cuff tears are the most common upper extremity condition seen by primary care and orthopaedic surgeons, with a spectrum ranging from tendinopathy to full-thickness tears with arthritic change. Some tears are traumatic, but most rotator cuff problems are degenerative. Not all tears are symptomatic and not all progress, and many patients in whom tears become more extensive do not experience symptom worsening. Hence, a standard algorithm for managing patients is challenging. The pathophysiology of rotator cuff tears is complex and encompasses an interplay between the tendon, bone and muscle. Rotator cuff tears begin as degenerative changes within the tendon, with matrix disorganization and inflammatory changes. Subsequently, tears progress to partial-thickness and then full-thickness tears. Muscle quality, as evidenced by the overall size of the muscle and intramuscular fatty infiltration, also influences symptoms, tear progression and the outcomes of surgery. Treatment depends primarily on symptoms, with non-operative management sufficient for most patients with rotator cuff problems. Modern arthroscopic repair techniques have improved recovery, but outcomes are still limited by a lack of understanding of how to improve tendon to bone healing in many patients.

Characteristics and Outcomes of US Veterans With Immunocompromised Conditions at High Risk of SARS-CoV-2 Infection With or Without Receipt of Oral Antiviral Agents.

OBJECTIVES: Molnupiravir and nirmatrelvir-ritonavir were the first oral antiviral agents to demonstrate reduced hospitalization or death in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but patients with immunocompromised conditions were not well-represented. The objective of this study was to characterize and compare the clinical outcomes of US veterans with immunocompromised conditions prescribed oral antivirals with those who did not receive oral antivirals for mild-to-moderate SARS-CoV-2 active infection. METHODS: This was a retrospective, observational, nationwide propensity-matched analysis of US veterans with immunocompromised conditions who developed documented SARS-CoV-2 infection. The primary outcome was the composite of any hospitalization or death within 30 days of diagnosis. Secondary outcomes included 30-day comparative rates of (1) any hospitalization, (2) death, (3) intensive care requirement, and (4) subset analyses of outcomes by oral antiviral used and vaccination status. RESULTS: The composite primary outcome was significantly lower in patients receiving oral antiviral therapy compared with those who did not (23/390 [5.9%] vs 57/390 [14.6%]; odds ratio, 0.37; 95% confidence interval, .22-.61). This difference was driven largely by fewer deaths in the oral antiviral group (1/390 [0.3%] vs 19/390 [4.9%]; odds ratio, 0.05; 95% confidence interval, .007-.38). There was no significant difference in rate of intensive care requirement. The composite outcome was improved in vaccinated patients (completing the first series or first booster dose) who received oral antiviral agents compared with those who did not receive oral antiviral agents. Compared with those prescribed nirmatrelvir-ritonavir, patients given molnupiravir were older, had a higher incidence of cautions/contraindications, greater prevalence of tobacco use, and more cardiovascular complications. CONCLUSIONS: Use of molnupiravir or nirmatrelvir-ritonavir was associated with lower incidences of hospitalization or death within 30 days of diagnosis in US veterans with immunocompromised conditions, regardless of vaccination status. These findings support the use of either oral antiviral in this patient population.

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.

Artificial intelligence-based applications in shoulder surgery leaves much to be desired: a systematic review.

Background: Artificial intelligence (AI) aims to simulate human intelligence using automated computer algorithms. There has been a rapid increase in research applying AI to various subspecialties of orthopedic surgery, including shoulder surgery. The purpose of this review is to assess the scope and validity of current clinical AI applications in shoulder surgery literature. Methods: A systematic literature review was conducted using PubMed for all articles published between January 1, 2010 and June 10, 2022. The search query used the terms as follows: (artificial intelligence OR machine learning OR deep learning) AND (shoulder OR shoulder surgery OR rotator cuff). All studies that examined AI application models in shoulder surgery were included and evaluated for model performance and validation (internal, external, or both). Results: A total of 45 studies were included in the final analysis. Eighteen studies involved shoulder arthroplasty, 13 rotator cuff, and 14 other areas. Studies applying AI to shoulder surgery primarily involved (1) automated imaging analysis including identifying rotator cuff tears and shoulder implants (2) risk prediction analyses including perioperative complications, functional outcomes, and patient satisfaction. Highest model performance area under the curve ranged from 0.681 (poor) to 1.00 (perfect). Only 2 studies reported external validation. Conclusion: Applications of AI in the field of shoulder surgery are expanding rapidly and offer patient-specific risk stratification for shared decision-making and process automation for resource preservation. However, model performance is modest and external validation remains to be demonstrated, suggesting increased scientific rigor is warranted prior to deploying AI-based clinical applications.

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 guide to appropriately planning and conducting meta-analyses: part 3. Special considerations-the network meta-analysis.

The meta-analysis has become one of the predominant studies designs in orthopaedic literature. Within recent years, the network meta-analysis has been implicated as a powerful approach to comparing multiple treatments for an outcome of interest when conducting a meta-analysis (as opposed to two competing treatments which is typical of a traditional meta-analysis). With the increasing use of the network meta-analysis, it is imperative for readers to possess the ability to independently and critically evaluate these types of studies. The purpose of this article is to provide the necessary foundation of knowledge to both properly conduct and interpret the results of a network meta-analysis.

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.

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.

Survival analyses and their applications in orthopaedics.

Survival analyses are a powerful statistical tool used to analyse data when the outcome of interest involves the time until an event. There is an array of models fit for this goal; however, there are subtle differences in assumptions, as well as a number of pitfalls, that can lead to biased results if researchers are unaware of the subtleties. As larger amounts of data become available, and more survival analyses are published every year, it is important that healthcare professionals understand how to evaluate these models and apply them into their practice. Therefore, the purpose of this study was to present an overview of survival analyses, including required assumptions and important pitfalls, as well as examples of their use within orthopaedic surgery.

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

Deep learning has the potential to be one of the most transformative technologies to impact orthopedic surgery. Substantial innovation in this area has occurred over the past 5 years, but clinically meaningful advancements remain limited by a disconnect between clinical and technical experts. That is, it is likely that few orthopedic surgeons possess both the clinical knowledge necessary to identify orthopedic problems, and the technical knowledge needed to implement deep learning-based solutions. To maximize the utilization of rapidly advancing technologies derived from deep learning models, orthopedic surgeons should understand the steps needed to design, organize, implement, and evaluate a deep learning project and its workflow. Equipping surgeons with this knowledge is the objective of this three-part editorial review. Part I described the processes involved in defining the problem, team building, data acquisition, curation, labeling, and establishing the ground truth. Building on that, this review (Part II) provides guidance on pre-processing and augmenting the data, making use of open-source libraries/toolkits, and selecting the required hardware to implement the pipeline. Special considerations regarding model training and evaluation unique to deep learning models relative to "shallow" machine learning models are also reviewed. Finally, guidance pertaining to the clinical deployment of deep learning models in the real world is provided. As in Part I, the focus is on applications of deep learning for computer vision and imaging.

Advancements in Artificial Intelligence for Foot and Ankle Surgery: A Systematic Review.

Background: There has been a rapid increase in research applying artificial intelligence (AI) to various subspecialties of orthopaedic surgery, including foot and ankle surgery. The purpose of this systematic review is to (1) characterize the topics and objectives of studies using AI in foot and ankle surgery, (2) evaluate the performance of their models, and (3) evaluate their validity (internal or external validation). Methods: A systematic literature review was conducted using PubMed/MEDLINE and Embase databases in December 2022. All studies that used AI or its subsets machine learning (ML) and deep learning (DL) in the setting of foot and ankle surgery relevant to orthopaedic surgeons were included. Studies were evaluated for their demographics, subject area, outcomes of interest, model(s) tested, model(s)' performance, and validity (internal or external). Results: A total of 31 studies met inclusion criteria: 14 studies investigated AI for image interpretation, 13 studies investigated AI for clinical predictions, and 4 studies were grouped as "other." Studies commonly explored AI for ankle fractures, calcaneus fractures, hallux valgus, Achilles tendon pathologies, plantar fasciitis, and sports injuries. For studies reporting the area under the receiver operating characteristic curve (AUC), AUCs ranged from 0.64 (poor) to 0.99 (excellent). Two studies (6.45%) reported external validation. Conclusion: Applications of AI in the field of foot and ankle surgery are expanding, particularly for image interpretation and clinical predictions. Current model performances range from poor to excellent, and most studies lack external validation, demonstrating a need for further research prior to deploying AI-based clinical applications. Level of Evidence: Level III, retrospective cohort study.

Editorial Commentary: Machine Learning Is Just a Statistical Technique, Not a Mystical Methodology or Peer Review Panacea.

Orthopaedic and sports medicine research surrounding artificial intelligence (AI) has dramatically risen over the last 4 years. Meaningful application and methodologic rigor in the scientific literature are critical to ensure appropriate use of AI. Common but critical errors for those engaging in AI-related research include failure to 1) ensure the question is important and previously unknown or unanswered; 2) establish that AI is necessary to answer the question; and 3) recognize model performance is more commonly a reflection of the data than the AI itself. We must take care to ensure we are not repackaging and internally validating registry data. Instead, we should be critically appraising our data-not the AI-based statistical technique. Without appropriate guardrails surrounding the use of artificial intelligence in Orthopaedic research, there is a risk of repackaging registry data and low-quality research in a recursive peer-reviewed loop.

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.

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.