Objective: To identify whether a single session of postrace dry needling can decrease postrace soreness and quantity of postrace leg cramps in half-marathon and full-marathon runners. Design: Single-blind, prospective, randomized, controlled trial. Setting: Finish line of 2018 Salt Lake City Marathon & Half-Marathon. Participants: Runners aged 18 years or older who completed a marathon or half-marathon. Interventions: True or sham dry needling of the bilateral vastus medialis and soleus muscles within 1 hour of race completion by 2 experienced practitioners. Main Outcome Measures: The primary outcome measure was numeric pain rating improvements for soreness on days 1, 2, 3, and 7 compared to immediately postrace. Secondary outcome measures included number of postrace cramps and subjective improvement of soreness. Results: Sixty-two runners were included with 28 receiving true and 34 receiving sham dry needling. Objective pain scores showed an increase in pain of the soleus muscles at days 1 and 2 (P ≤ 0.003 and P ≤ 0.041, respectively) in the dry needling group. No differences were seen in postrace pain in the vastus medialis muscles (P > 0.05). No association was seen between treatment group and presence of postrace cramping at any time point (P > 0.05). Subjectively, there was a nonsignificant trend for those receiving dry needling to feel better than expected over time (P = 0.089), but no difference with cramping (P = 0.396). Conclusions: A single postrace dry needling session does not objectively improve pain scores or cramping compared to sham therapy.

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Background BackgroundVarious methods of sham procedures have been used in controlled trials evaluating dry needling efficacy although few have performed validation studies of the sham procedure.

Background: In the era of personalized medicine, a major challenge is harnessing longitudinal data across the cancer care continuum, which includes multimodal data sets of biologic, molecular, and clinical information about patients (pts) and their tumors. There is a growing need for new computing analytics, such as machine learning–an important tool in healthcare bio-informatics. We report our approach to building cancer disease models in an unbiased manner through utilization of a causal machine learning and simulation platform. Methods: The Swedish Cancer Institute (SCI) Personalized Medicine Research Program (PMRP) is a prospective registration protocol with the objective of establishing a centralized longitudinal, molecular, and phenotypic data repository. Since 2014, over 1,030 pts have been enrolled, having undergone next-generation sequencing (NGS) profiling of their tumors. Of these pts, we identified 100 breast cancer pts who also have detailed longitudinal clinical annotation within our SCI Breast Cancer Registry. All de-identified data, variables, and data points in the multimodal data types are integrated into normalized data frames to include demographics, cancer risks, tumor specifications, tumor sequencing, initial and subsequent cancer treatments, and outcomes data. A reverse engineering approach, via the Reverse Engineering and Forward Simulation (REFS) platform, is being utilized, focusing on discovering the complex causal mechanisms that determine which therapies will produce the best outcomes for an individual pt. This method goes beyond traditional approaches that rely on data correlations to match treatments to pts. The breast cancer causal model uncovers many of the possible combinations of causal relationships that drive outcomes and enables “what if?” simulations of a variety of interventions, across pts, to determine optimal therapies. Performance metrics and model robustness will be explored using a stratified, n-fold (e.g., 10-fold) cross-validation procedure, which is designed to provide an unbiased estimate of model generalization to new observations. Results: The causal model and simulations can elevate the providers' abilities to better understand treatment responses based on pts' unique clinical data and mutational statuses; study different treatment options to optimize management; and understand the complex interactions among variables that lead to a range of treatment outcomes. Conclusions: Knowledge generated from the simulations of the disease model can potentially streamline and support the clinical decision-making process, to include molecular tumor board deliberations, and ultimately assist providers in arriving at optimal treatment recommendations for pts. Citation Format: Henry Kaplan, Anna Berry, Kristine Rinn, Erin Ellis, George Birchfield, Tanya Wahl, Xiaoyu Liu, Mariko Tameishi, J D. Beatty, Patricia Dawson, Vivek Mehta, Anna Holman, Mary Atwood, Shlece Alexander, Candy Bonham, Lauren Summers, Iya Khalil, Boris Hayete, Diane Wuest, Wei Zheng, Yuhang Liu, Xulong Wang, Thomas David Brown. Machine learning approach to personalized medicine in breast cancer patients: Development of data-driven, personalized, causal modeling through identification and understanding of optimal treatments for predicting better disease outcomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5299.

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