Application of Machine Learning Algorithms to Predict Clinically Meaningful Improvement After Arthroscopic Anterior Cruciate Ligament Reconstruction.

Background: Understanding specific risk profiles for each patient and their propensity to experience clinically meaningful improvement after anterior cruciate ligament reconstruction (ACLR) is important for preoperative patient counseling and management of expectations.

Purpose: To develop machine learning algorithms to predict achievement of the minimal clinically important difference (MCID) on the International Knee Documentation Committee (IKDC) score at a minimum 2-year follow-up after ACLR.

Study Design: Case-control study; Level of evidence, 3.

The painful shoulder in the throwing athlete.

Successful nonoperative treatment of shoulder injuries in the throwing athlete requires a team approach. The physician, physical therapist, and athletic trainer work together with the athlete to determine a diagnosis and design a treatment protocol. Injury recovery requires restoration of muscle strength and balance followed by a careful return to a throwing program.

An anatomic study of the subscapularis tendon and myotendinous junction.

The subscapularis muscle and tendon were studied in 25 cadaveric specimens. The subscaputaris insertion into the proximal humerus and its musculolendinous junction was defined. We found that the subscapularis muscle had a gradual formation of the musculotendinous junction and, in contrast to classical descriptions, inserted into the humerus as both tendon and muscle.