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Background: Underprivileged and underserved patients from developing countries often present late with advanced, untreated spinal deformities. We report a three-stage all-posterior approach using limited skeletal traction with Gardner-Wells tongs (GWTs) for the management of severe idiopathic scoliosis during a humanitarian surgical mission trip., Observations: A 17-year-old high-school female was previously diagnosed with juvenile idiopathic scoliosis (diagnosed at age 8) and progressed to a severe 135° kyphoscoliosis. Procedural stage 1 involved spinal instrumentation and posterior releases via posterior column osteotomies from T3 to L4. She then underwent 7 days of skeletal traction with GWTs in the intensive care unit as stage 2. In stage 3, rod engagement, posterior spinal fusion, and partial T10 vertebral column resection were performed. There were no changes in intraoperative neuromonitoring during either surgery and she woke up neurologically intact after both stages of the surgical procedure., Lessons: Skeletal traction with GWTs is a viable alternative to traditional halo-gravity traction in settings with limited resources. Three-stage spinal deformity correction using limited skeletal traction is a feasible and effective approach for managing severe scoliosis during humanitarian surgical mission trips.

Prediction of air pollutant levels plays an important role in the regulatory plans aimed at the control and reduction of airborne pollutants such as fine particulate matter (PM). Deterministic photochemical air quality models, which are commonly used for regulatory management and planning, are computationally intensive and also expensive for routine predictions. Compared to deterministic photochemical air quality models, data-driven statistical models are simpler and may be more accurate. In this paper, hidden Markov models (HMM) are used to forecast daily average PM(2.5) concentrations 24 h ahead. In conventional HMM applications, observation distributions emitted from certain hidden states are assumed as having Gaussian distributions. However, certain key meteorological factors and most PM(2.5) precursors exhibit a non-Gaussian distribution in reality, which would degrade the HMM performance significantly. In order to address this problem, in this paper, HMMs with log-normal, Gamma and generalized extreme value (GEV) distributions are developed to predict PM(2.5) concentration at Concord and Sacramento monitors in Northern California. Results show that HMM with non-Gaussian emission distributions is able to predict PM(2.5) exceedance days correctly and reduces false alarms dramatically. Compared to HMM with Gaussian distributions, HMM with log-normal distributions can improve the true prediction rate (TPR) by 37.5% and reduce the false alarms by 78% at Concord. And HMM with GEV distribution can improve TPR by 150% and reduce false alarms by 63.62% at Sacramento Del Paso Manor. Comparisons between different distributions used in HMM show that the closer the distribution employed in HMM is to the observation sequence, the better the model prediction performance., (Copyright © 2012 Elsevier B.V. All rights reserved.)