Background: As football comes under greater scrutiny because of concussions and other injuries, many people are searching for safer alternatives, especially for youth athletes. The injury rate in TackleBar football, one such alternative, is not currently known. Purpose: To describe TackleBar football and calculate its injury rate over a single season to compare it with both traditional tackle and flag football. Study Design: Descriptive epidemiology study. Methods: TackleBar football was described, and injuries of male athletes (age range, 9-14 years) participating in TackleBar football were tracked over the course of a season (August 5 through October 27, 2018) and compared with published injury rates for traditional tackle and flag football. Results: The overall injury rate of TackleBar football was found to be 0.31 per 1000 athlete-exposures (AEs), which is lower than youth tackle (2.25-2.60/1000 AEs) and flag (5.77/1000 AEs) football. Conclusion: TackleBar football may represent a reasonable alternative to tackle and flag football with regard to injuries.

Reporting of sports-related concussions (SRCs) has risen dramatically over the last decade, increasing awareness of the need for treatment and prevention of SRCs. To date most prevention studies have focused on equipment and rule changes to sports in order to reduce the risk of injury. However, increased neck strength has been shown to be a predictor of concussion rate. In the TRAIN study, student-athletes will follow a simple neck strengthening program over the course of three years in order to better understand the relationship between neck strength and SRCs. Neck strength of all subjects will be measured at baseline and biannually over the course of the study using a novel protocol. Concussion severity and duration in any subject who incurs an SRC will be evaluated using the Sports Concussion Assessment Tool 5th edition, a questionnaire based tool utilizing several tests that are commonly affected by concussion, and an automated eye tracking algorithm. Neck strength, and improvement of neck strength, will be compared between concussed and non-concussed athletes to determine if neck strength can indeed reduce risk of concussion. Neck strength will also be analyzed taking into account concussion severity and duration to find if a strengthening program can provide a protective factor to athletes. The study population will consist of student-athletes, ages 12–23, from local high schools and colleges. These athletes are involved in a range of both contact and non-contact sports.

PURPOSE There are currently limited objective criteria to help assist physicians in determining whether an individual patient with acute myeloid leukemia (AML) is likely to do better with induction with either standard 7 + 3 chemotherapy or targeted therapy with venetoclax plus azacitidine. The study goal was to address this need by developing exploratory clinical decision support methods. PATIENTS AND METHODS Univariable and multivariable analysis as well as comparison of a range of machine learning (ML) predictors were performed using cohorts of 120 newly diagnosed 7 + 3-treated AML patients compared with 101 venetoclax plus azacitidine–treated patients. RESULTS A variety of features in the two patient cohorts were identified that may potentially correlate with short- and long-term outcomes, toxicities, and other considerations. A subset of these diagnostic features was then used to develop ML-based predictors with relatively high areas under the curve of short- and long-term outcomes, hospital stays, transfusion requirements, and toxicities for individual patients treated with either venetoclax/azacitidine or 7 + 3. CONCLUSION Potential ML-based approaches to clinical decision support to help guide individual patients with newly diagnosed AML to either 7 + 3 or venetoclax plus azacitidine induction therapy were identified. Larger cohorts with separate test and validation studies are necessary to confirm these initial findings.