Outcomes following inpatient rehabilitation of lower limb amputees

Introduction: Lower-limb amputations are a serious adverse consequence of lifestyle related chronic conditions and a serious concern among the aging population in Australia. Lower limb amputations have severe personal, social and economic impacts on the individual, healthcare system and broader community. This study aimed to address a critical gap in the research literature by investigating the physical functioning and social characteristics of lower limb amputees at discharge from tertiary hospital inpatient rehabilitation. Method: A cohort study was implemented among patients with lower limb amputations admitted to a Geriatric Assessment and Rehabilitation Unit for rehabilitation at a tertiary hospital. Conventional descriptive statistics were used to examine patient demographic, physical functioning and social living outcomes recorded for patients admitted between 2005 and 2011. Results: A total of 423 admissions occurred during the study period, 313 (74%) were male. This sample included admissions for left (n = 189, 45%), right (n = 220, 52%) and bilateral (n = 14, 3%) lower limb amputations, with 15 (3%) patients dying whilst an inpatient. The mean (standard deviation) age was 65 (13.9) years. Amputations attributed to vascular causes accounted for 333 (78%) admissions; 65 (15%) of these had previously had an amputation. The mean (SD) length of stay in the rehabilitation unit was 56 (42) days. Prior to this admission, 123 (29%) patients were living alone, 289 (68%) were living with another and 3 (0.7%) were living in residential care. Following this amputation related admission, 89 (21%) patients did not return to their prior living situation. Of those admitted, 187 (44%) patients were discharged with a lower limb prosthesis. Conclusion: The clinical group is predominately older adults. The ratio of males to females was approximately 3:1. Over half did not return to walking and many were not able to return to their prior accommodation. However, few patients died during their admission.

Monitoring smartphones for anomaly detection

In this paper we demonstrate how to monitor a smartphone running Symbian operating system and Windows Mobile in order to extract features for anomaly detection. These features are sent to a remote server because running a complex intrusion detection system on this kind of mobile device still is not feasible due to capability and hardware limitations. We give examples on how to compute relevant features and introduce the top ten applications used by mobile phone users based on a study in 2005. The usage of these applications is recorded by a monitoring client and visualized. Additionally, monitoring results of public and self-written malwares are shown. For improving monitoring client performance, Principal Component Analysis was applied which lead to a decrease of about 80 of the amount of monitored features.

Analyzing the effectiveness of graph metrics for anomaly detection in online social networks

Online social networks can be modelled as graphs; in this paper, we analyze the use of graph metrics for identifying users with anomalous relationships to other users. A framework is proposed for analyzing the effectiveness of various graph theoretic properties such as the number of neighbouring nodes and edges, betweenness centrality, and community cohesiveness in detecting anomalous users. Experimental results on real-world data collected from online social networks show that the majority of users typically have friends who are friends themselves, whereas anomalous users’ graphs typically do not follow this common rule. Empirical analysis also shows that the relationship between average betweenness centrality and edges identifies anomalies more accurately than other approaches.

Monitoring android for collaborative anomaly detection : a first architectural draft

Our daily lives become more and more dependent upon smartphones due to their increased capabilities. Smartphones are used in various ways, e.g. for payment systems or assisting the lives of elderly or disabled people. Security threats for these devices become more and more dangerous since there is still a lack of proper security tools for protection. Android emerges as an open smartphone platform which allows modification even on operating system level and where third-party developers first time have the opportunity to develop kernel-based low-level security tools. Android quickly gained its popularity among smartphone developers and even beyond since it bases on Java on top of "open" Linux in comparison to former proprietary platforms which have very restrictive SDKs and corresponding APIs. Symbian OS, holding the greatest market share among all smartphone OSs, was even closing critical APIs to common developers and introduced application certification. This was done since this OS was the main target for smartphone malwares in the past. In fact, more than 290 malwares designed for Symbian OS appeared from July 2004 to July 2008. Android, in turn, promises to be completely open source. Together with the Linux-based smartphone OS OpenMoko, open smartphone platforms may attract malware writers for creating malicious applications endangering the critical smartphone applications and owners privacy. Since signature-based approaches mainly detect known malwares, anomaly-based approaches can be a valuable addition to these systems. They base on mathematical algorithms processing data that describe the state of a certain device. For gaining this data, a monitoring client is needed that has to extract usable information (features) from the monitored system. Our approach follows a dual system for analyzing these features. On the one hand, functionality for on-device light-weight detection is provided. But since most algorithms are resource exhaustive, remote feature analysis is provided on the other hand. Having this dual system enables event-based detection that can react to the current detection need. In our ongoing research we aim to investigates the feasibility of light-weight on-device detection for certain occasions. On other occasions, whenever significant changes are detected on the device, the system can trigger remote detection with heavy-weight algorithms for better detection results. In the absence of the server respectively as a supplementary approach, we also consider a collaborative scenario. Here, mobile devices sharing a common objective are enabled by a collaboration module to share information, such as intrusion detection data and results. This is based on an ad-hoc network mode that can be provided by a WiFi or Bluetooth adapter nearly every smartphone possesses.

Solving inherent problems of anomaly detection by cooperation

Anomaly detection compensates shortcomings of signature-based detection such as protecting against Zero-Day exploits. However, Anomaly Detection can be resource-intensive and is plagued by a high false-positive rate. In this work, we address these problems by presenting a Cooperative Intrusion Detection approach for the AIS, the Artificial Immune System, as an example for an anomaly detection approach. In particular we show, how the cooperative approach reduces the false-positive rate of the detection and how the overall detection process can be organized to account for the resource constraints of the participating devices. Evaluations are carried out with the novel network simulation environment NeSSi as well as formally with an extension to the epidemic spread model SIR

SHOX gene is expressed in vertebral body growth plates in idiopathic and congenital scoliosis : implications for the etiology of scoliosis in Turner Syndrome

Reduced SHOX gene expression has been demonstrated to be associated with all skeletal abnormalities in Turner syndrome, other than scoliosis (and kyphosis). There is evidence to suggest that Turner syndrome scoliosis is clinically and radiologically similar to idiopathic scoliosis, although the phenotypes are dissimilar. This pilot gene expression study used relative quantitative real-time PCR (qRT-PCR) of the SHOX (short stature on X) gene to determine whether it is expressed in vertebral body growth plates in idiopathic and congenital scoliosis. After vertebral growth plate dissection, tissue was examined histologically and RNA was extracted and its integrity was assessed using a Bio-Spec Mini, NanoDrop ND-1000 spectrophotometer and standard denaturing gel electrophoresis. Following cDNA synthesis, gene-specific optimization in a Corbett RotorGene 6000 real-time cycler was followed by qRT-PCR of vertebral tissue. Histological examination of vertebral samples confirmed that only growth plate was analyzed for gene expression. Cycling and melt curves were resolved in triplicate for all samples. SHOX abundance was demonstrated in congenital and idiopathic scoliosis vertebral body growth plates. SHOX expression was 11-fold greater in idiopathic compared to congenital (n = 3) scoliosis (p = 0.027). This study confirmed that SHOX was expressed in vertebral body growth plates, which implies that its expression may also be associated with the scoliosis (and kyphosis) of Turner syndrome. SHOX expression is reduced in Turner syndrome (short stature). In this study, increased SHOX expression was demonstrated in idiopathic scoliosis (tall stature) and congenital scoliosis.

The incidence and predictors of lower limb split skin graft failure and primary closure dehiscence in day case surgical patients

This project was an observational study of outpatients following lower limb surgical procedures for removal of skin cancers. Findings highlight a previously unreported high surgical site failure rate. Results also identified four potential risk factors (increasing age, presence of leg pain, split skin graft and haematoma) which negatively impact on surgical site healing in this population.

Quality of life of women with lower limb swelling or lymphedema 3-5 years following endometrial cancer

Objective To quantitatively assess and compare the quality of life (QoL) of women with a self-reported diagnosis of lower limb lymphedema (LLL), to women with lower limb swelling (LLS), and to women without LLL or LLS following treatment for endometrial cancer. Methods 1399 participants in the Australian National Endometrial Cancer Study were sent a follow-up questionnaire 3–5 years after diagnosis. Women were asked if they had experienced swelling in the lower limbs and, if so, whether they had received a diagnosis of lymphedema by a health professional. The 639 women who responded were categorised as: Women with LLL (n = 68), women with LLS (n = 177) and women without LLL or LLS (n = 394). Multivariable-adjusted generalized linear models were used to compare women’s physical and mental QoL by LLL status. Results On average, women were 65 years of age and 4 years after diagnosis. Women with LLL had clinically lower physical QoL (M= 41.8, SE= 1.4) than women without LLL or LLS (M= 45.1, SE= 0.8, p = .07), however, their mental QoL was within the normative range (M= 49.6; SE= 1.1 p = 1.0). Women with LLS had significantly lower physical (M= 41.0, SE= 1.0, p = .003) and mental QoL (M= 46.8; SE= 0.8, p < .0001) than women without LLL or LLS (Mental QoL: M= 50.6, SE= 0.8). Conclusion Although LLL was associated with reductions in physical QoL, LLS was related to reductions in both physical and mental QoL 3-5 years after cancer treatment. Early referral to evidence-based lymphedema programs may prevent long-term impairments to women’s QoL.

A rule-based hybrid method for anomaly detection in online-social-network graphs

Detecting anomalies in the online social network is a significant task as it assists in revealing the useful and interesting information about the user behavior on the network. This paper proposes a rule-based hybrid method using graph theory, Fuzzy clustering and Fuzzy rules for modeling user relationships inherent in online-social-network and for identifying anomalies. Fuzzy C-Means clustering is used to cluster the data and Fuzzy inference engine is used to generate rules based on the cluster behavior. The proposed method is able to achieve improved accuracy for identifying anomalies in comparison to existing methods.

Clinician-driven automated classification of limb fractures from free-text radiology reports

The aim of this research is to report initial experimental results and evaluation of a clinician-driven automated method that can address the issue of misdiagnosis from unstructured radiology reports. Timely diagnosis and reporting of patient symptoms in hospital emergency departments (ED) is a critical component of health services delivery. However, due to disperse information resources and vast amounts of manual processing of unstructured information, a point-of-care accurate diagnosis is often difficult. A rule-based method that considers the occurrence of clinician specified keywords related to radiological findings was developed to identify limb abnormalities, such as fractures. A dataset containing 99 narrative reports of radiological findings was sourced from a tertiary hospital. The rule-based method achieved an F-measure of 0.80 and an accuracy of 0.80. While our method achieves promising performance, a number of avenues for improvement were identified using advanced natural language processing (NLP) techniques.

Automatic classification of free-text radiology reports to identify limb fractures using machine learning and the SNOMED CT ontology

Objective To develop and evaluate machine learning techniques that identify limb fractures and other abnormalities (e.g. dislocations) from radiology reports. Materials and Methods 99 free-text reports of limb radiology examinations were acquired from an Australian public hospital. Two clinicians were employed to identify fractures and abnormalities from the reports; a third senior clinician resolved disagreements. These assessors found that, of the 99 reports, 48 referred to fractures or abnormalities of limb structures. Automated methods were then used to extract features from these reports that could be useful for their automatic classification. The Naive Bayes classification algorithm and two implementations of the support vector machine algorithm were formally evaluated using cross-fold validation over the 99 reports. Result Results show that the Naive Bayes classifier accurately identifies fractures and other abnormalities from the radiology reports. These results were achieved when extracting stemmed token bigram and negation features, as well as using these features in combination with SNOMED CT concepts related to abnormalities and disorders. The latter feature has not been used in previous works that attempted classifying free-text radiology reports. Discussion Automated classification methods have proven effective at identifying fractures and other abnormalities from radiology reports (F-Measure up to 92.31%). Key to the success of these techniques are features such as stemmed token bigrams, negations, and SNOMED CT concepts associated with morphologic abnormalities and disorders. Conclusion This investigation shows early promising results and future work will further validate and strengthen the proposed approaches.

Automated classification of limb fractures from free-text radiology reports using a clinician-informed gazetteer methodology

Background Timely diagnosis and reporting of patient symptoms in hospital emergency departments (ED) is a critical component of health services delivery. However, due to dispersed information resources and a vast amount of manual processing of unstructured information, accurate point-of-care diagnosis is often difficult. Aims The aim of this research is to report initial experimental evaluation of a clinician-informed automated method for the issue of initial misdiagnoses associated with delayed receipt of unstructured radiology reports. Method A method was developed that resembles clinical reasoning for identifying limb abnormalities. The method consists of a gazetteer of keywords related to radiological findings; the method classifies an X-ray report as abnormal if it contains evidence contained in the gazetteer. A set of 99 narrative reports of radiological findings was sourced from a tertiary hospital. Reports were manually assessed by two clinicians and discrepancies were validated by a third expert ED clinician; the final manual classification generated by the expert ED clinician was used as ground truth to empirically evaluate the approach. Results The automated method that attempts to individuate limb abnormalities by searching for keywords expressed by clinicians achieved an F-measure of 0.80 and an accuracy of 0.80. Conclusion While the automated clinician-driven method achieved promising performances, a number of avenues for improvement were identified using advanced natural language processing (NLP) and machine learning techniques.

Design of a traumatic injury simulator for assessing lower limb response to high loading rates

Current military conflicts are characterized by the use of the improvised explosive device. Improvements in personal protection, medical care, and evacuation logistics have resulted in increasing numbers of casualties surviving with complex musculoskeletal injuries, often leading to lifelong disability. Thus, there exists an urgent requirement to investigate the mechanism of extremity injury caused by these devices in order to develop mitigation strategies. In addition, the wounds of war are no longer restricted to the battlefield; similar injuries can be witnessed in civilian centers following a terrorist attack. Key to understanding such mechanisms of injury is the ability to deconstruct the complexities of an explosive event into a controlled, laboratory-based environment. In this article, a traumatic injury simulator, designed to recreate in the laboratory the impulse that is transferred to the lower extremity from an anti-vehicle explosion, is presented and characterized experimentally and numerically. Tests with instrumented cadaveric limbs were then conducted to assess the simulator’s ability to interact with the human in two mounting conditions, simulating typical seated and standing vehicle passengers. This experimental device will now allow us to (a) gain comprehensive understanding of the load-transfer mechanisms through the lower limb, (b) characterize the dissipating capacity of mitigation technologies, and (c) assess the bio-fidelity of surrogates.

Use of cadavers and anthropometric test devices (ATDs) for assessing lower limb injury outcome from under‐vehicle explosions

Lower extremities are particularly susceptible to injury in an under‐vehicle explosion. Operational fitness of military vehicles is assessed through anthropometric test devices (ATDs) in full‐scale blast tests. The aim of this study was to compare the response between the Hybrid‐III ATD, the MiL‐Lx ATD and cadavers in our traumatic injury simulator, which is able to replicate the response of the vehicle floor in an under‐vehicle explosion. All specimens were fitted with a combat boot and tested on our traumatic injury simulator in a seated position. The load recorded in the ATDs was above the tolerance levels recommended by NATO in all tests; no injuries were observed in any of the 3 cadaveric specimens. The Hybrid‐III produced higher peak forces than the MiL‐Lx. The time to peak strain in the calcaneus of the cadavers was similar to the time to peak force in the ATDs. Maximum compression of the sole of the combat boot was similar for cadavers and MiL‐Lx, but significantly greater for the Hybrid‐III. These results suggest that the MiL‐Lx has a more biofidelic response to under‐vehicle explosive events compared to the Hybrid‐III. Therefore, it is recommended that mitigation strategies are assessed using the MiL‐Lx surrogate and not the Hybrid‐III.

Cross cultural adaptation and validation of a Spanish version of the lower limb functional index

Background: The Lower Limb Functional Index (LLFI) is a relatively recently published regional outcome measure. The development article showed the LLFI had robust and valid clinimetric properties with sound psychometric and practical characteristics when compared to the Lower Limb Extremity Scale (LEFS) criterion standard. Objective: The purpose of this study was cross cultural adaptation and validation of the LLFI Spanish-version (LLFI-Sp) in a Spanish population. Methods: A two stage observational study was conducted. The LLFI was initially cross-culturally adapted to Spanish through double forward and single backward translation; then subsequently validated for the psychometric characteristics of validity, internal consistency, reliability, error score and factor structure. Participants (n = 136) with various lower limb conditions of >12 weeks duration completed the LLFI-Sp, Western Ontario and McMaster University Osteoarthritis Index (WOMAC) and the Euroqol Health Questionnaire 5 Dimensions (EQ-5D-3 L). The full sample was employed to determine internal consistency, concurrent criterion validity, construct validity and factor structure; a subgroup (n = 45) determined reliability at seven days concurrently completing a global rating of change scale. Results: The LLFI-Sp demonstrated high but not excessive internal consistency (α = 0.91) and high reliability (ICC = 0.96). The factor structure was one-dimensional which supported the construct validity. Criterion validity with the WOMAC was strong (r = 0.77) and with the EQ-5D-3 L fair and inversely correlated (r = -0.62). The study limitations included the lack of longitudinal data and the determination of responsiveness. Conclusions: The LLFI-Sp supports the findings of the original English version as being a valid lower limb regional outcome measure. It demonstrated similar psychometric properties for internal consistency, validity, reliability, error score and factor structure.