Double fascicular nerve transfer (DFNT) involves transferring fascicles from the median and ulnar nerves to the musculocutaneous nerve to restore elbow flexion. We performed this procedure on a patient who was diagnosed with neuralgic amyotrophy (NA) 2 years previously. We aimed to investigate the short-term outcomes and feasibility of DFNT applied to non-traumatic neuropathy. A 63-year-old, right-handed man presented to us with persistent right elbow flexion weakness 24 months after receiving the diagnosis of NA. On examination, biceps strength was 0/5, and shoulder abduction strength was 3/5. Magnetic resonance neurography demonstrated muscle atrophy in the upper trunk. DFNT was considered to restore elbow flexion. The donor and recipient fascicles were then coapted (flexor digitorum superficialis to biceps brachii, flexor carpi ulnaris to brachialis). Postoperatively, paresthesia was reported in the area of ulnar innervation, and the patient complained of hand clumsiness. From postoperative day 10 onward, biceps muscle contracture was identified and on the 14th day, grade 3 elbow flexion strength was observed. We suggest that DFNT may be a safe and effective method to restore elbow flexion in patients with NA.

We aimed to evaluate the accuracy of the classification of carotid plaque vulnerability (unstable vs. stable plaques) by neurosurgical residents based on carotid ultrasonography (US) images. Methods: A total of 405 subjects with 995 images were included in the study. Using a neuroradiologist's decision as the reference value, the classification results of five reviewers were analyzed. The sensitivity, specificity, and overall accuracy were estimated. Then, a pairwise comparison of the receiver operating characteristic (ROC) curve and precision-recall curve was performed to compare the reviewers' classification accuracy. Results:The mean age of the subjects was 70.5 years (range, 44-91 years) and 223 (55.1%) were female. The number of unstable and stable plaques was 236 (24.7%) and 749 (75.3%), respectively. The best-balanced classification performance of plaque vulnerability was a sensitivity of 83.7% (95% confidence interval [CI], 78.5%-88.1%), specificity of 69.0% (95% CI, 65.6%-72.3%), and overall accuracy of 72.7% (95% CI, 69.8%-75.4%). The best ROC performance was an area under the curve (AUC) of 0.583 (95% CI, 0.552-0.614). The precision-recall curve also showed low classification accuracy among the reviewers (AUC difference: 0.028; 95% bootstrap CI, 0.007-0.048). Conclusion:The classification accuracy of neurosurgical residents to discriminate plaque vulnerability seen on carotid US images was low in a real-world clinical setting. Thus, it is necessary to develop systems that help to educate and automatically interpret plaque stability.