Arthroscopy Honors Award-Winning Articles and Authors: Our Annual Research Awards

Artificial Intelligence and Machine Learning May Resolve Health Care Information Overload

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.

Arthroscopy Honors 2023 Award Winning Publications and Authors

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.

Minimally Clinically Important Difference (MCID) Is a Low Bar

Patients don't care about "statistical" significance. Patient-centered outcome measures focus on "clinical" significance and include minimal clinically important difference (MCID), patient acceptable symptomatic state (PASS), substantial clinical benefit (SCB), and maximal outcome improvement (MOI). "Minimal" is a low bar. MCID neither addresses whether patients are satisfied nor whether they have derived a substantial benefit. MCID is commonly reported allowing comparison between studies, and MCID can be calculated retrospectively, so reporting MCID is acceptable. However, we also need to report PASS, SCB, and, in unique patients like high-level athletes, we may also need to report MOI to adjust for high pretreatment scores and a ceiling effect. Finally, threshold scores are patient-level metrics and must be reported as percentage of patients who meet the threshold, not reported as to whether, as a group, the mean score for the cohort meets the threshold or not (which is a common error).