Efficacy of interspinous device versus surgical decompression in the treatment of lumbar spinal stenosis: a modified network analysis.

Study designSystematic review using a modified network analysis.ObjectivesTo compare the effectiveness and morbidity of interspinous-device placement versus surgical decompression for the treatment of lumbar spinal stenosis.SummaryTraditionally, the most effective treatment for degenerative lumbar spinal stenosis is through surgical decompression. Recently, interspinous devices have been used in lieu of standard laminectomy.MethodsA review of the English-language literature was undertaken for articles published between 1970 and March 2010. Electronic databases and reference lists of key articles were searched to identify studies comparing surgical decompression with interspinous-device placement for the treatment of lumbar spinal stenosis. First, studies making the direct comparison (cohort or randomized trials) were searched. Second, randomized controlled trials (RCTs) comparing each treatment to conservative management were searched to allow for an indirect comparison through a modified network analysis approach. Comparison studies involving simultaneous decompression with placement of an interspinous device were not included. Studies that did not have a comparison group were not included since a treatment effect could not be calculated. Two independent reviewers assessed the strength of evidence using the GRADE criteria assessing quality, quantity, and consistency of results. The strengths of evidence for indirect comparisons were downgraded. Disagreements were resolved by consensus.ResultsWe identified five studies meeting our inclusion criteria. No RCTs or cohort studies were identified that made the direct comparison of interspinous-device placement with surgical decompression. For the indirect comparison, three RCTs compared surgical decompression to conservative management and two RCTs compared interspinous-device placement to conservative management. There was low evidence supporting greater treatment effects for interspinous-device placement compared to decompression for disability and pain outcomes at 12 months. There was low evidence demonstrating little to no difference in treatment effects between the groups for walking distance and complication rates.ConclusionThe indirect treatment effect for disability and pain favors the interspinous device compared to decompression. The low evidence suggests that any further research is very likely to have an important impact on the confidence in the estimate of effect and is likely to change the estimate. No significant treatment effect differences were observed for postoperative walking distance improvement or complication rates; however, findings should be considered with caution because of indirect comparisons and short follow-up periods

Efficient Estimation of Word Representations in Vector Space

We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities

Towards Robust Design and Training of Deep Neural Networks

Currently neural networks run as software, which typically requires expensive GPU resources. As the adoption of deep learning continues for a more diverse range of applications, direct hardware implemented neural networks (HNN) will provide deep learning solutions at far lower hardware requirements. However, Gaussian noise along hardware connections degrades model accuracy, an issue this research seeks to resolve using a novel analog error correcting code (ECC). To aid in developing noise tolerant deep neural networks (DNN), this research also investigates the impact of loss functions on training. This involves alternating multiple loss functions throughout training, aiming to prevent local optimals. The effects on training time and final accuracy are then analyzed. This research investigates analog ECCs and loss function variation to allow for future noise tolerant HNN networks. ECC results demonstrate three to five decibel improvements to model accuracy when correcting Gaussian noise. Loss variation results demonstrate a correlation between loss function similarity and training performance. Other correlations are also presented and addressed

Doctor of Philosophy

dissertationExchanging patient specific information across heterogeneous information systems is a critical but increasingly complex and expensive challenge. Lacking a universal unique identifier for healthcare, patient records must be linked using combinations of identity attributes such as name, date of birth, and sex. A state's birth certificate registry contains demographic information that is potentially very valuable for identity resolution, but its use for that purpose presents numerous problems. The objectives of this research were to: (1) assess the frequency, extent, reasons, and types of changes on birth certificates; (2) develop and evaluate an ontology describing information used in identity resolution; and (3) use a logical framework to model identity transactions and assess the impact of policy decisions in a cross jurisdictional master person index. To understand birth certificate changes, we obtained de identifified datasets from the Utah birth certifificate registry, including history and reasons for changes from 2000 to 2012. We conducted cohort analyses, examining the number, reason, and extent of changes over time, and cross sectional analyses to assess patterns of changes. We evaluated an ontological approach to overcome heterogeneity between systems exchanging identity information and demonstrated the use of two existing ontologies, the Simple Event Model (SEM) and the Clinical Element Model (CEM), to capture an individual's identity history. We used Discrete Event Calculus to model identity events iv across domains and over time. Models were used to develop contextual rules for releasing minimal information from birth certificate registries for sensitive cases such as adoptions. Our findings demonstrate that the mutability of birth certificates makes them a valuable resource for identity resolution, provided that changes can be captured and modeled in a usable form. An ontology can effectively model identity attributes and the events that cause them to change over time, as well as to overcome syntactic and semantic heterogeneity. Finally, we show that dynamic, contextual rules can be used to govern the flow of identity information between systems, allowing entities to link records in the most difficult cases, avoid costly human review, and avoid the threats to privacy that come from such review

An experimental study of the effects of army recruitment television advertising on high school seniors

In order for Army recruiters in the Appalachian region to effectively reach their target audience through television advertising, they must know which segment within the overall stated target audience of high school seniors between the ages of 17 to 21 are most likely to be influenced by Army recruitment advertising. Recruiters must also determine the type of programming preferred by the primary target segment to enable recruiting messages to be placed in programming where it is likely to have the most impact and achieve maximum frequency of exposure among the most receptive audience

Genetic and germplasm studies with several isoenzyme loci in soybean (Glycine max [L.] Merr)

The genetics of several electrophoretically detected isoenzyme variants in soybean (Glycine max L. Merr.) were investigated. Four loci conditioning aconitase (aconitate hydratase) mobility variants, as detected on starch gels, were defined. The four loci and their codominant alleles are: Aco1 (a, b), Aco2 (a, b), Aco3 (a, b), and Aco4 (a, b, c). The Enp locus, which conditions endopeptidase mobility variants, was described, and has two codominant alleles, a and b, as detected on starch gels. The Sod2 locus, with codominant alleles a and b, conditions superoxide dismutase mobility variants detected on polyacrylamide gels. All six of these loci are ideal markers for use in soybean genetics and breeding research;The six new loci were used in linkage studies, along with several other isoenzyme and morphological loci, to expand the genetic map of soybean. Both the Sp1 locus, which conditions (beta)-amylase variants, and the Aco3 locus, were assigned to linkage group 1. Sp1 is linked to the T locus (pubescence color) with a recombination frequency of 30.8 (+OR-) 1.6%, and to Y12 (chlorophyll deficient) with a frequency of 19.4 (+OR-) 1.8%. Aco3 is linked to both Sp1 and T, but the frequency of recombination varied widely with the parentage of the cross;The alleles present at 13 loci conditioning isoenzyme variants were determined in 1339 accessions in the USDA cultivated soybean (G. max) and wild soybean (G. soja Sieb. et Zucc.) germplasm collections. Several loci showed definite geographical trends in their distribution. There were differences in allozyme frequency between G. max and G. soja, as well, particularly for the loci Ap, Idh1, and Sod2. Based on gene frequencies and occurrence of multiple-locus genotypes, G. max and G. soja seem to represent different gene pools. Cluster analysis of the isozyme database defined groups of similar accessions across geographic origins. This information will make it possible to select parental materials of diverse origin for genetic and breeding research

Subgrouped Real Time Recurrent Learning Neural Networks

A subgrouped Real Time Recurrent Learning (RTRL) network was evaluated. The one layer net successfully learns the XOR problem, and can be trained to perform time dependent functions. The net was tested as a predictor on the behavior of a signal, based on past behavior. While the net was not able to predict the signal\u27s future behavior, it tracked the signal closely. The net was also tested as a classifier for time varying phenomena; for the differentiation of five classes of vehicle images based on features extracted from the visual information. The net achieved a 99.2% accuracy in recognizing the five vehicle classes. The behavior of the subgrouped RTRL net was compared to the RTRL network described in Capt R. Lindsey\u27s AFIT Master\u27s thesis. The subgrouped RTRL performance proved close to the RTRL network in accuracy while reducing the time required to train the network for multiple output (classification) problems