Kerlan-Jobe Orthopaedic Clinic overhead athlete scores in asymptomatic professional baseball pitchers.

BACKGROUND: The Kerlan-Jobe Orthopaedic Clinic (KJOC) Shoulder and Elbow score is a subjective questionnaire that has been validated and been shown to be more specific in overhead athletes than the American Shoulder and Elbow Surgeons scale. The purpose of this study was to determine a mean KJOC score and reasonable range of KJOC scores within which a healthy asymptomatic professional baseball pitcher will fall. It was hypothesized that healthy professional baseball pitchers would have very high KJOC scores. MATERIALS AND METHODS: KJOC questionnaires were given to all healthy pitchers before the start of the season at all levels in 1 professional Minor League system. Pitchers were asked to complete the questionnaire upon reporting to their AAA, AA, or A affiliate team. Any pitcher starting the season on the disabled list was excluded from the study. RESULTS: KJOC scores were returned by 44 pitchers. The mean score for all pitchers was 94.82 (95% confidence interval, 92.94-96.70). The mean score for each question was greater than 9 of 10. The mean score for the AAA affiliate was significantly higher than that for the AA affiliate (P = .015). No other significant differences in scores were found between class levels or groups based on professional playing experience. CONCLUSION: Only 7 of 44 healthy asymptomatic pitchers (16%) had a KJOC score below 90. Therefore, we believe that the KJOC score is an accurate assessment for overhead athletes and normal values should be greater than 90. Anything below this value could be a potential cause for concern for team physicians. LEVEL OF EVIDENCE: Basic Science, Survey Study, Healthy Subjects

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development. In order for AI developers to earn trust from system users, customers, civil society, governments, and other stakeholders that they are building AI responsibly, they will need to make verifiable claims to which they can be held accountable. Those outside of a given organization also need effective means of scrutinizing such claims. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems and their associated development processes, with a focus on providing evidence about the safety, security, fairness, and privacy protection of AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms