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Abstract Idecabtagene vicleucel (Ide-cel) has demonstrated excellent efficacy and durable responses in patients with relapsed/refractory multiple myeloma (RRMM). However, the outcomes with ide-cel in patients with extramedullary disease (EMD) remain incompletely characterized. We included patients with RRMM treated with ide-cel between May 2021 and April 2023 across 11 US academic institutions. Visceral or soft tissue lesions non-contiguous from bone was classified as EMD. Time-to-event analyses were performed from date of ide-cel infusion. Among 351 patients, 84 (24%) had EMD prior to infusion. The median follow-up from ide-cel infusion was 18.2 months (95% CI: 17-19.3). The day 90 overall response rates (ORR) were 52% vs. 82% for the EMD and non-EMD cohorts, respectively (p

Impulsivity is an important behavioral trait in dogs that affects many aspects of their relationship with humans. But how well do owners know their dog’s levels of impulsivity? Two studies have investigated how owner perceptions of their dog’s impulsivity correlate with the distance traveled in a spatial impulsivity task requiring choices between smaller, closer vs. larger, more distant food treats (Brady et al., 2018; Mongillo et al., 2019). However, these studies have demonstrated mixed results. The current project aimed to replicate these studies by correlating owner responses to the Dog Impulsivity Assessment Survey (DIAS) and the dog’s maximum distance traveled in a spatial impulsivity task. We found that neither the DIAS overall score nor its three subcomponent scores correlated with dogs’ distance traveled. This result replicates Mongillo et al.’s lack of a relationship but does not replicate Brady et al.’s effect, questioning the generalizability of owner reports of dog impulsivity. The lack of replication could result from differences in methodology and sample populations, but it raises intriguing questions about possible differences in dog characteristics and owner knowledge of their dogs across cultures.

To be effective, a prescribed prosthetic device must match the functional requirements and capabilities of each patient. These capabilities are usually assessed by a clinician and reported by the Medicare K-level designation of mobility. However, it is not clear how the K-level designation objectively relates to the use of prostheses outside of a clinical environment. Here, we quantify participant activity using mobile phones and relate activity measured during real world activity to the assigned K-levels. We observe a correlation between K-level and the proportion of moderate to high activity over the course of a week. This relationship suggests that accelerometry-based technologies such as mobile phones can be used to evaluate real world activity for mobility assessment. Quantifying everyday activity promises to improve assessment of real world prosthesis use, leading to a better matching of prostheses to individuals and enabling better evaluations of future prosthetic devices.

Fall prevention is a critical component of health care; falls are a common source of injury in the elderly and are associated with significant levels of mortality and morbidity. Automatically detecting falls can allow rapid response to potential emergencies; in addition, knowing the cause or manner of a fall can be beneficial for prevention studies or a more tailored emergency response. The purpose of this study is to demonstrate techniques to not only reliably detect a fall but also to automatically classify the type. We asked 15 subjects to simulate four different types of falls-left and right lateral, forward trips, and backward slips-while wearing mobile phones and previously validated, dedicated accelerometers. Nine subjects also wore the devices for ten days, to provide data for comparison with the simulated falls. We applied five machine learning classifiers to a large time-series feature set to detect falls. Support vector machines and regularized logistic regression were able to identify a fall with 98% accuracy and classify the type of fall with 99% accuracy. This work demonstrates how current machine learning approaches can simplify data collection for prevention in fall-related research as well as improve rapid response to potential injuries due to falls.