Evaluation Methods and Measurement Challenges for Industrial Exoskeletons.

In recent years, exoskeleton test methods for industrial exoskeletons have evolved to include simulated laboratory and field environments. Physiological, kinematic, and kinetic metrics, as well as subjective surveys, are used to evaluate exoskeleton usability. In particular, exoskeleton fit and usability can also impact the safety of exoskeletons and their effectiveness at reducing musculoskeletal injuries. This paper surveys the state of the art in measurement methods applied to exoskeleton evaluation. A notional classification of the metrics based on exoskeleton fit, task efficiency, comfort, mobility, and balance is proposed. In addition, the paper describes the test and measurement methods used in supporting the development of exoskeleton and exosuit evaluation methods to assess their fit, usability, and effectiveness in industrial tasks such as peg in hole, load align, and applied force. Finally, the paper includes a discussion of how the metrics can be applied towards a systematic evaluation of industrial exoskeletons, current measurement challenges, and future research directions.

The Use of Machine Learning to Create a Risk Score to Predict Survival in Patients with Hepatocellular Carcinoma: A TCGA Cohort Analysis.

Introduction: Hepatocellular carcinoma (HCC) accounts for approximately 90% of primary liver malignancies and is currently the fourth most common cause of cancer-related death worldwide. Due to varying underlying etiologies, the prognosis of HCC differs greatly among patients. It is important to develop ways to help stratify patients upon initial diagnosis to provide optimal treatment modalities and follow-up plans. The current study uses Artificial Neural Network (ANN) and Classification Tree Analysis (CTA) to create a gene signature score that can help predict survival in patients with HCC. Methods: The Cancer Genome Atlas (TCGA-LIHC) was analyzed for differentially expressed genes. Clinicopathological data were obtained from cBioPortal. ANN analysis of the 75 most significant genes predicting disease-free survival (DFS) was performed. Next, CTA results were used for creation of the scoring system. Cox regression was performed to identify the prognostic value of the scoring system. Results: 363 patients diagnosed with HCC were analyzed in this study. ANN provided 15 genes with normalized importance >50%. CTA resulted in a set of three genes (NRM, STAG3, and SNHG20). Patients were then divided in to 4 groups based on the CTA tree cutoff values. The Kaplan-Meier analysis showed significantly reduced DFS in groups 1, 2, and 3 (median DFS: 29.7 months, 16.1 months, and 11.7 months, p < 0.01) compared to group 0 (median not reached). Similar results were observed when overall survival (OS) was analyzed. On multivariate Cox regression, higher scores were associated with significantly shorter DFS (1 point: HR 2.57 (1.38-4.80), 2 points: 3.91 (2.11-7.24), and 3 points: 5.09 (2.70-9.58), p < 0.01). Conclusion: Long-term outcomes of patients with HCC can be predicted using a simplified scoring system based on tumor mRNA gene expression levels. This tool could assist clinicians and researchers in identifying patients at increased risks for recurrence to tailor specific treatment and follow-up strategies for individual patients.

Patient perception of scoliosis correction surgery on Instagram.

OBJECTIVE: Patient feedback surveys provide important insight into patient outcomes, satisfaction, and perioperative needs. Recent critiques have questioned provider-initiated surveys and their capacity to accurately gauge patient perspectives due to intrinsic biases created by question framing. In this study, the authors sought to evaluate provider-independent, patient-controlled social media Instagram posts in order to better understand the patient experience following scoliosis correction surgery. METHODS: Twitter and Instagram were queried for posts with two tagged indicators, #scoliosissurgery or @scoliosissurgery, resulting in no relevant Twitter posts and 25,000 Instagram posts. Of the initial search, 24,500 Instagram posts that did not directly involve the patient's own experience were eliminated. Posts were analyzed and coded for the following criteria: the gender of the patient, preoperative or postoperative timing discussed in the post, and classified themes related to the patient's experiences with scoliosis correction surgery. RESULTS: Females made 87.6% of the Instagram posts about their experience following scoliosis correction surgery. The initial postoperative stage of surgery was mentioned in 7.6% of Instagram posts. The most common theme on Instagram involved offering or seeking online support from other patients, which constituted 85.2% of all posts. Other common themes included concern about the surgical scar (31.8%), discussing the results of treatment (28.8%), and relief regarding results (21.2%). CONCLUSIONS: Social media provided a platform to analyze unprompted feedback from patients. Patients were most concerned with their scoliosis correction surgery in the period of time 2 weeks or more after surgery. Themes that were most commonly found on Instagram posts were offering or seeking online support from other patients and concern about the surgical scar. Patient-controlled social media platforms, like Instagram, may provide a useful mechanism for healthcare providers to understand the patient experience following scoliosis correction surgery. Such platforms may help in evaluating postoperative satisfaction and improving postoperative quality of care.