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Ultrasound (US) is a valuable imaging modality used to detect primary breast malignancy. However, radiologists have a limited ability to distinguish between benign and malignant lesions on US, leading to false-positive and false-negative results, which limit the positive predictive value of lesions sent for biopsy (PPV3) and specificity. A recent study demonstrated that incorporating an AI-based decision support (DS) system into US image analysis could help improve US diagnostic performance. While the DS system is promising, its efficacy in terms of its impact also needs to be measured when integrated into existing clinical workflows. The current study evaluates workflow schemas for DS integration and its impact on diagnostic accuracy. The impact on two different reading methodologies, sequential and independent, was assessed. This study demonstrates significant accuracy differences between the two workflow schemas as measured by area under the receiver operating curve (AUC), as well as inter-operator variability differences as measured by Kendall’s tau-b. This evaluation has practical implications on the utilization of such technologies in diagnostic environments as compared to previous studies.

Background: Low back pain is a prevalent condition that causes a substantial health burden. Despite intensive and expensive clinical efforts, its prevalence is growing. Nonpharmacologic treatments are effective at improving painrelated outcomes; however, treatment effect sizes are often modest. Physical therapy (PT) and cognitive behavioral therapy (CBT) have the most consistent evidence of effectiveness. Growing evidence also supports mindfulnessbased approaches. Discussions with providers and patients highlight the importance of discussing and trying options to find the treatment that works for them and determining what to do when initial treatment is not successful. Herein, we present the protocol for a study that will evaluate evidence-based, protocol-driven treatments using PT, CBT, or mindfulness to examine comparative effectiveness and optimal sequencing for patients with chronic low back pain. Methods: The Optimized Multidisciplinary Treatment Programs for Nonspecific Chronic Low Back Pain (OPTIMIZE) Study will be a multisite, comparative effectiveness trial using a sequential multiple assessment randomized trial design enrolling 945 individuals with chronic low back pain. The co-primary outcomes will be disability (measured using the Oswestry Disability Index) and pain intensity (measured using the Numerical Pain Rating Scale). After baseline assessment, participants will be randomly assigned to PT or CBT. At week 10, participants who have not experienced at least 50% improvement in disability will be randomized to cross-over phase-1 treatments (e.g., PT to CBT) or to Mindfulness-Oriented Recovery Enhancement (MORE). Treatment will consist of 8 weekly sessions. Longterm outcome assessments will be performed at weeks 26 and 52.

Speech perception and memory for speech require active engagement. Gestural theories have emphasized mainly the effect of speaker's movements on speech perception. They fail to address the effects of listener movement, focusing on communication as a boundary condition constraining movement among interlocutors. The present work attempts to break new ground by using multifractal geometry of physical movement as a common currency for supporting both sides of the speaker–listener dyads. Participants self-paced their listening to a narrative, after which they completed a test of memory querying their narrative comprehension and their ability to recognize words from the story. The multifractal evidence of nonlinear interactions across timescales predicted the fluency of speech perception. Self-pacing movements that enabled listeners to control the presentation of speech sounds constituted a rich exploratory process. The multifractal nonlinearity of this exploration supported several aspects of memory for the perceived spoken language. These findings extend the role of multifractal geometry in the speaker's movements to the narrative case of speech perception. In addition to posing novel basic research questions, these findings make a compelling case for calibrating multifractal structure in text-to-speech synthesizers for better perception and memory of speech.