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The Fragility Index (FI) is defined as the number of patients whose outcome would need to change to reverse a statistically significant finding to a nonsignificant finding. The FI is nothing more than a repackaging of statistical significance based on the P value, perpetuating (1) ignoring of results that are "not" statistically significant; (2) treating results that are statistically significant as certain; and (3) distracting from evaluation of clinical significance. A well-designed trial includes a sample size calculation to determine the minimum number of patients required to observe a difference between study groups (if a difference exists). By including this minimum number, clinical trials are designed to be fragile, yet subsequently criticized as such, leading readers to the incorrect conclusion that the studies are flawed. It's time to move past systematic reviews focused on the FI. [ABSTRACT FROM AUTHOR]

Essential elements required for proper use of artificial intelligence machine learning tools in biomedical research and scientific publications include (1) explanation justifying why a machine learning approach contributes to the purpose of the study; (2) description of the adequacy of the data (input) to produce the desired results (output); (3) details of the algorithmic (i.e., computational) approach including methods for organizing the data (preprocessing); the machine learning computational algorithm(s) assessed; on what data the models were trained; the presence of bias and efforts to mitigate these effects; and the methods for quantifying the variables (features) most influential in determining the results (e.g., Shapley values); (4) description of methods, and reporting of results, quantitating performance in terms of both model accuracy and model calibration (level of confidence in the model's predictions); (5) availability of the programming code (including a link to the code when available—ideally, the code should be available); (6) discussion of model internal validation (results applicable and sensitive to the population investigated and data on which the model was trained) and external validation (were the results investigated as to whether they are generalizable to different populations? If not, consideration of this limitation and discussion of plans for external validation, i.e., next steps). As biomedical research submissions using artificial intelligence technology increase, these requirements could facilitate purposeful use and comprehensive methodological reporting. [ABSTRACT FROM AUTHOR]

Arthroscopy Techniques is home to technical notes with video on all methods and procedures arthroscopic or related at https://www.arthroscopytechniques.org and https://www.youtube.com/@ArthroscopyTechniques. ATech publications run the gamut from classic to innovative, but where to start if one is a trainee, new in practice, or wishing to expand the breadth of one's practice? To fill this "gap," we introduce the Foundations of Arthroscopy Techniques Collection (https://www.arthroscopytechniques.org/foundations-of-arthroscopy-techniques-collection) featuring technical notes with video detailing the essential fundamentals required to perform arthroscopy of the shoulder, elbow, wrist, hip, knee, and foot & ankle. [ABSTRACT FROM AUTHOR]

Disaggregation, in the medical literature, means separation into demographic groups. This results in an opportunity to discover differences in outcomes by group, which could improve future treatments and provide outcome data, by group, that could be included in future systematic reviews. In research, the term disaggregation is most often used in reference to addressing inequities. We support the Sex and Gender Equity Research (SAGER) guidelines and encourage authors to examine how sex and gender are taken into account in their study and ensure adequate representation by sex and gender. (We respect that not all studies can or are designed to capture data by sex and gender, and that gender is "complex" and "fluid.") Disaggregation is encouraged, when possible, for other demographic variables allowing evaluation of all marginalized (as well as nonmarginalized) populations, so that we can better care for patients. [ABSTRACT FROM AUTHOR]

Biomedical information doubles almost every 2 months, and this very rate is expected to double by 2025. The result is information overload for clinicians and researchers. Today, artificial intelligence (AI) and machine learning (ML) research contribute to the deluge of information. In addition, AI large language models, although capable of automating scientific writing, are flawed. They hallucinate (make things up), are trained primarily on non−peer-reviewed content, raise ethical and legal issues, and lack human empathy. Still, when it comes to AI including ML, we are optimistic. The technology is improving rapidly. In the future, AI will help us manage unwieldy information by processing data, determining diagnoses, recommending treatments, and predicting outcomes. In research, AI and ML similarly promise efficient data analysis and literature review and will create new content in response to our instructions. Human touch will be required, and we will disclose use of AI proactively, including rationale for its use, our data input, our level of confidence in the output, and the patients or populations to whom the output may be applied. In addition, we will ensure data quality is high and bias is minimized. Most of all, we will provide essential reasoning, clinical and research guidance, and diligent oversight. Humans will remain accountable. [ABSTRACT FROM AUTHOR]

With earnest appreciation to the Arthroscopy Association of North America Education Foundation for its generous support for Arthroscopy's Annual Awards, we announce the 2023 publications determined to represent the best clinical research, basic science research, resident/fellow research, and systematic reviews published in 2023, as well as the most downloaded and most cited articles published 5 years ago. This year, we also present the inaugural award for the Most Viewed Arthroscopy Techniques article and video published 5 years ago, as well as our best Podcasts of 2023. [ABSTRACT FROM AUTHOR]

Auld lang syne; times long past. We were unsuspecting of the coming pandemic. Things changed. COVID-19 impacted each of us, our families, our patients, our medical practices, and our daily lives. The year 2020 was one of cancellation and loss; 2021 and 2022 were years of adaptation, caution, and continued loss. Finally, 2023 began a time of recovery; while COVID-19 is still with us, the pandemic has been declared ended, and we emerge with gratitude. We slowly removed our masks and rediscovered the vitality of meeting face-to-face. And throughout, Arthroscopy has continued to develop, including new journals; special issues on rehabilitation, biologics, and diversity as they relate to musculoskeletal care; collaboration with the American Board of Orthopaedic Surgery, Maintenance of Certification, Web-Based Longitudinal Assessment Pathway; Arthroscopy Techniques ; Infographics; Visual Abstracts; and Podcasts. At year end, we extend special thanks to editors whose terms have expired, our team and leaders, our reviewers, our authors and readers, and our patients who offer us their trust and an opportunity to serve. [ABSTRACT FROM AUTHOR]

Despite a push for a focus on clinical rather than "statistical" significance and an emphasis on reporting of outcome thresholds such as the patient acceptable symptomatic state (PASS) and substantial clinical benefit (SCB), the PASS and SCB are rarely reported and, when reported, are often reported incorrectly. Yet, patients require satisfaction (PASS) as a result of our treatments, and patients desire to improve substantially (SCB). Determining whether patients are satisfied and/or substantially improved is simple... just ask them. The questions are known as anchor questions. Obviously, different patients have different PASS and SCB thresholds, and reliance on previously published literature for values of these thresholds can result in error—thus, the anchor questions. And, each patient must be assessed individually. Outcome thresholds are not group-level metrics, and they must be reported as the percentage of individuals who achieve the clinically significant outcome. Certain patients, such as athletes, have high baseline function and may demand maximum outcome improvement. In contrast, the minimal clinically important difference is a less-than-ideal measure; patients do not desire "minimal" improvement. Journals must do a better job of publishing patient-reported outcome measures that matter most to patients: satisfaction and substantial benefit. [ABSTRACT FROM AUTHOR]

The 2024 Annual Meeting of the Arthroscopy Association of North America in Boston was our largest ever. The program was innovative, and attendees departed with a sense of purpose and new knowledge. Besides intellectual exchange, the meeting fostered connections that will shape arthroscopy in years to come. We look forward to AANA25 in Washington, DC. [ABSTRACT FROM AUTHOR]

Orthopaedic biologics holds great promise. Absent peer-reviewed clinical musculoskeletal research, orthobiologics indications and treatment recommendations will remain opaque. In a Call for Papers, Arthroscopy ; Arthroscopy Techniques ; and Arthroscopy, Sports Medicine, and Rehabilitation editors invite authors to submit clinical musculoskeletal biologics original scientific research, and technical notes with video. Each year, the top articles will be awarded inclusion in an annual Biologics Special Issue. Future investigators and current readers could follow the science while being mindful of the regulatory environment. [ABSTRACT FROM AUTHOR]