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Expansion duraplasty to reopen effaced subarachnoid space and improve spinal cord perfusion, autoregulation, and spinal pressure reactivity index (sPRX) has been advocated in patients with traumatic cervical spinal cord injury (tCSCI). We designed this study to identify candidates for expansion duraplasty, based on the absence of cerebrospinal fluid (CSF) interface around the spinal cord on magnetic resonance imaging (MRI), in the setting of otherwise adequate bony decompression. Over a 61-month period, 104 consecutive American Spinal Injury Association Impairment Scale (AIS) grades A-C patients with tCSCI had post-operative MRI to assess the adequacy of surgical decompression. Their mean age was 53.4 years, and 89% were male. Sixty-one patients had falls, 31 motor vehicle collisions, 11 sport injuries, and one an assault. The AIS grade was A in 56, B in 18, and C in 30 patients. Fifty-four patients had fracture dislocations; there was no evidence of skeletal injury in 50 patients. Mean intramedullary lesion length (IMLL) was 46.9 (standard deviation = 19.4) mm. Median time from injury to decompression was 17 h (interquartile range 15.2 h). After surgery, 94 patients had adequate decompression as judged by the presence of CSF anterior and posterior to the spinal cord, whereas 10 patients had effacement of the subarachnoid space at the injury epicenter. In two patients whose decompression was not definitive and post-operative MRI indicated inadequate decompression, expansion duraplasty was performed. Candidates for expansion duraplasty (i.e., those with inadequate decompression) were significantly younger (

Nearly 300,000 older adults experience a hip fracture every year, the majority of which occur following a fall. Unfortunately, recovery after fall-related trauma such as hip fracture is poor, where older adults diagnosed with Alzheimer's Disease and Related Dementia (ADRD) spend a particularly long time in hospitals or rehabilitation facilities during the post-operative recuperation period. Because older adults value functional recovery and spending time at home versus facilities as key outcomes after hospitalization, identifying factors that influence days spent at home after hospitalization is imperative. While several individual-level factors have been identified, the characteristics of the treating hospital have recently been identified as contributors. However, few methodological rigorous approaches are available to help overcome potential sources of bias such as hospital-level unmeasured confounders, informative hospital size, and loss to follow-up due to death. This article develops a useful tool equipped with unsupervised learning to simultaneously handle statistical complexities that are often encountered in health services research, especially when using large administrative claims databases. The proposed estimator has a closed form, thus only requiring light computation load in a large-scale study. We further develop its asymptotic properties that can be used to make statistical inference in practice. Extensive simulation studies demonstrate superiority of the proposed estimator compared to existing estimators., Comment: Contact Emails: chixiang.chen@som.umaryland.edu