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BACKGROUND: The BIOCHIP is an indirect immunofluorescence diagnostic investigation which identifies multiple autoantibodies with a mosaic panel of target antigen-specific substrates in a single incubation field. The EUROIMMUN Dermatology Profile ELISA allows simultaneous investigation of the six most important autoantibodies in bullous autoimmune dermatoses. Evaluation of the BIOCHIP Mosaic 7, compared to that of the EUROIMMUN Dermatology Profile ELISA, when used as a diagnostic investigation in pemphigus and pemphigoid, was undertaken in an Australian cohort. METHODS: The serum of 27 patients was analysed including patients with pemphigus vulgaris (n = 10), pemphigus foliaceous (n = 4), bullous pemphigoid (n = 8), mucous membrane pemphigoid (n = 3) and negative controls (n = 2). Results of the BIOCHIP were compared with the EUROIMMUN Dermatology Profile ELISA, as well as with histology, direct immunofluorescence and indirect immunofluorescence. RESULTS: In pemphigus vulgaris, sensitivity & specificity for the BIOCHIP Mosaic 7 were 100% and 94.1%, comparable to that of the EUROIMMUN Dermatology Profile ELISA with 80% sensitivity and 100% specificity. In bullous pemphigoid, sensitivity of the BIOCHIP was 87.5% and sensitivity of the EUROIMMUN Dermatology ELISA profile was 75%, whilst specificities for both diagnostic methods were 100% in our limited cohort. There was substantial or almost perfect concordance between the BIOCHIP Mosaic 7 and EUROIMMUN Dermatology Profile ELISA for pemphigus vulgaris and bullous pemphigoid. CONCLUSION: The BIOCHIP Mosaic 7 is a rapid, reliable diagnostic investigation in pemphigus and bullous pemphigoid. Results indicate it is comparable to the EUROIMMUN Dermatology Profile ELISA, whilst also providing additional testing with salt split skin, on one field.

Objective There have been few descriptions of how outpatient cancer care is provided to patients from culturally and linguistically diverse (CALD) communities. As populations who experience disparities in cancer care access and outcomes, deeper understanding is needed to help identify those factors which can shape the receipt of multidisciplinary care in ambulatory settings. This paper reports on data collected and analysed as part of a multicentre characterisation of care in Australian public hospital cancer outpatient clinics (OPCs). Results Analysis of data from our ethnographic study of four OPCs identified three themes: “Identifying CALD patient language-related needs”; “Capacity and resources to meet CALD patient needs”, and “Making it work for CALD communities.” The care team comprises not only clinicians but also families and non-clinical staff; OPCs serve as “touchpoints” facilitating access to a range of therapeutic services. The findings highlight the potential challenges oncology professionals negotiate in providing care to CALD communities and the ways in which clinicians adapt their practices, formulate strategies and use available resources to support care delivery.

OBJECTIVES: The Australian Ageing Semantic Differential (AASD) survey was developed to quantify medical student attitudes towards older people. The purpose of this study is to examine psychometric properties of the survey and confirm its factor structure of four composites. DESIGN: A cross-sectional study. SETTING: Three medical schools in three Australian states: Victoria, Western Australia and South Australia. PARTICIPANTS: Third-year or fourth-year medical students (n=188, response rate=79%). OUTCOME MEASURES: In the previous AASD study, exploratory factor analysis supported a four-factor model consisting of 'Instrumentality' (I), 'Personal Appeal' (PA), 'Experience' (E) and 'Sociability' (S). Congeneric one-factor confirmatory factor analysis (CFA) were used to examine model fit for factors using a new student sample (n=188).Psychometric properties of survey items and factors.Post-hoc analysis of pooled data from this study and earlier AASD study (n=509). RESULTS: Indices of fit (Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), root mean square error of approximation (RMSEA), standardised root mean square residual (SRMR)) for data to the factor model were: PA adequate fit (CFI=0.94, TLI=0.89, RMSEA=0.11 and SRMR=0.05), I good fit (CFI=0.99, TLI=0.99, RMSEA=0.04 and SRMR=0.03), S good fit (CFI=0.98, TLI=0.95, RMSEA=0.06 and SRMR=0.03) and E excellent fit (CFI=1.0, TLI=1.0, RMSEA=0.00 and SRMR=0.01).The AASD was internally consistent (Cronbach's alpha=0.84), without difference in mean student scores by institution. Mean AASD score was positive for medical students outside New South Wales (73.2/114).Mean I score for all Australian students was negative, with female respondents' mean E score significantly higher than their counterparts. A positive correlation between student age and I score was noted. CONCLUSIONS: The AASD is internally consistent and generalisable within Australia, with acceptable structural validity for measuring medical student attitudes to

The occurrence of mental fatigue during tasks like driving a vehicle increases risk of injury or death. Changes in electroencephalographic (EEG) activity associated with mental fatigue has been frequently studied and considered a promising biomarker of mental fatigue. This is despite differences in methodologies and outcomes in prior research. A systematic review with meta-analyses was conducted to establish the influence of mental fatigue on EEG activity spectral bands, and to determine in which regions fatigue-related EEG spectral changes are likely to occur. A high-yield search strategy identified 21 studies meeting inclusion criteria for investigating the change in EEG spectral activity in non-diseased adults engaged in mentally fatiguing tasks. A medium effect size (using Cohen's g) of 0.68 (95%CI: 0.24-1.13) was found for increase in overall EEG activity following mental fatigue. Further examination of individual EEG spectral bands and regions using network meta-analyses indicated large increases in theta (g = 1.03; 95%CI: 0.79-1.60) and alpha bands (g = 0.85; 95%CI: 0.47-1.43), with small to moderate changes found in delta and beta bands. Central regions of the scalp showed largest change (g = 0.80; 95%CI: 0.46-1.21). Sub-group analyses indicated large increases in theta activity in frontal, central and posterior sites (all g > 1), with moderate changes in alpha activity in central and posterior sites. Findings have implications for fatigue monitoring and countermeasures with support for change in theta activity in frontal, central and posterior sites as a robust biomarker of mental fatigue and change in alpha wave activity considered a second line biomarker to account for individual variability.

BACKGROUND:The adoption of Patient Reported Outcome Measures (PROMs) in cancer care has been widely advocated, but little is known about the evidence for the implementation of PROMs in practice. Qualitative research captures the perspectives of health professionals as end-users of PROMs and can be used to inform adoption efforts. This paper presents a systematic review and synthesis of qualitative research conducted to address the question: What are the attitudes of health professionals towards PROMs in oncology, including any barriers and facilitators to the adoption of PROMS, reported in qualitative evidence? METHODS:Systematic searches of qualitative evidence were undertaken in four databases and reviewed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies published in English between 1998 and 2018, which reported qualitative findings about the attitudes of health professionals working in oncology towards PROMs were eligible. Studies were assessed using the Critical Appraisal Skills Programme's Qualitative Research Checklist. A sentiment analysis was conducted on primary text to examine the polarity (neutral, positive or negative) of health professionals' views of PROMs. Qualitative meta-synthesis was conducted using a constant comparative analysis. RESULTS:From 1227 articles after duplicates were removed, with 1014 excluded against the screening criteria, 213 full text articles remained and were assessed; 34 studies met the inclusion criteria and were included. The majority of studies were of good quality. Sentiment analysis on primary text demonstrated an overall positive polarity from the expressed opinions of health professionals. The meta-synthesis showed health professionals' attitudes in four domains: identifying patient issues and needs using PROMs; managing and addressing patient issues; the care experience; and the integration of PROMs into clinical practice. CONCLUSIONS:From the accounts of health profession

© 2019 Author(s). Introduction An understanding of the real-world provision of oncology outpatient services can help maintain service quality in the face of escalating demand and tight budgets, by informing the design of interventions that improve the effectiveness or efficiency of provision. The aims of this study are threefold. First, to develop an understanding of cancer services in outpatient clinics by characterising the organisation and practice of multidisciplinary care (MDC). Second, to explore the key areas of: (a) clinical decision-making and (b) engagement with patients' supportive needs. Third, to identify barriers to, and facilitators of, the delivery of quality care in these settings. Methods and analysis A suite of mixed-methods studies will be implemented at six hospitals providing cancer outpatient clinics, with a staged roll-out. In Stage One, we will examine policies, use unstructured observations and undertake interviews with key health professionals to characterise the organisation and delivery of MDC. In Stage Two, observations of practice will continue, to deepen our understanding, and to inform two focused studies. The first will explore decision-making practices and the second will examine how staff engage with patients' needs; both studies involve interviews, to complement observation. As part of the study of supportive care, we will examine the implications of an introduction of patient-reported measures (PRMs) into care, adding surveys to interviews before and after PRMs roll-out. Data analysis will account for site-specific and cross-site issues using an adapted Qualitative Rapid Appraisal, Rigorous Analysis approach. Quantitative data from clinician surveys will be statistically analysed and triangulated with the related qualitative study findings. Ethics and dissemination Ethical approval was granted by South Eastern Sydney Local Health District Human Research Ethics Committee (no. 18/207). Findings will be shared with participating ho

© 2019 IEEE. This paper presents the combination of clustering-based independent component analysis (ICASSO) and power spectral density (PSD) as a features extractor of mental fatigue from spinal cord injury (SCI) patients. Initially, the results show that SCI and abled-bodied groups have no differences in EEG for alert and mental fatigue states. Further, the coefficient determination (R2) is calculated for testing the variation of data alert vs. fatigue on the SCI group, resulting in a lower R2 for proposed combination of ICASSO and PSD method compared to the PSD method only. With the lower R2 values, this shows that the proposed method ICASSO and PSD is able to provide superior distinction for separating fatigue vs. alert data variation. The statistical significance is found across four EEG bands and EEG channels.

Background: Predominantly antibody deficiencies (PADs) are the most common type of primary immunodeficiency in adults. PADs frequently pass undetected leading to delayed diagnosis, delayed treatment, and the potential for end-organ damage including bronchiectasis. In addition, PADs are frequently accompanied by comorbid autoimmune disease, and an increased risk of malignancy. Objective(s): To characterize the diagnostic and clinical features of adult PAD patients in Victoria, Australia. Method(s): We identified adult patients receiving, or having previously received immunoglobulin replacement therapy for a PAD at four hospitals in metropolitan Melbourne, and retrospectively characterized their clinical and diagnostic features. Result(s): 179 patients from The Royal Melbourne, Alfred and Austin Hospitals, and Monash Medical Centre were included in the study with a median age of 49.7 years (range: 16-87 years), of whom 98 (54.7%) were female. The majority of patients (116; 64.8%) met diagnostic criteria for common variable immunodeficiency (CVID), and 21 (11.7%) were diagnosed with X-linked agammaglobulinemia (XLA). Unclassified hypogammaglobulinemia (HGG) was described in 22 patients (12.3%), IgG subclass deficiency (IGSCD) in 12 (6.7%), and specific antibody deficiency (SpAD) in 4 individuals (2.2%). The remaining four patients had a diagnosis of Good syndrome (thymoma with immunodeficiency). There was no significant difference between the age at diagnosis of the disorders, with the exception of XLA, with a median age at diagnosis of less than 1 year. The median age of reported symptom onset was 20 years for those with a diagnosis of CVID, with a median age at diagnosis of 35 years. CVID patients experienced significantly more non-infectious complications, such as autoimmune cytopenias and lymphoproliferative disease, than the other antibody deficiency disorders. The presence of non-infectious complications was associated with significantly reduced survival in the c

BACKGROUND: Predominantly antibody deficiencies (PADs) are the most common type of primary immunodeficiency in adults. PADs frequently pass undetected leading to delayed diagnosis, delayed treatment, and the potential for end-organ damage including bronchiectasis. In addition, PADs are frequently accompanied by comorbid autoimmune disease, and an increased risk of malignancy. OBJECTIVES: To characterize the diagnostic and clinical features of adult PAD patients in Victoria, Australia. METHODS: We identified adult patients receiving, or having previously received immunoglobulin replacement therapy for a PAD at four hospitals in metropolitan Melbourne, and retrospectively characterized their clinical and diagnostic features. RESULTS: 179 patients from The Royal Melbourne, Alfred and Austin Hospitals, and Monash Medical Centre were included in the study with a median age of 49.7 years (range: 16-87 years), of whom 98 (54.7%) were female. The majority of patients (116; 64.8%) met diagnostic criteria for common variable immunodeficiency (CVID), and 21 (11.7%) were diagnosed with X-linked agammaglobulinemia (XLA). Unclassified hypogammaglobulinemia (HGG) was described in 22 patients (12.3%), IgG subclass deficiency (IGSCD) in 12 (6.7%), and specific antibody deficiency (SpAD) in 4 individuals (2.2%). The remaining four patients had a diagnosis of Good syndrome (thymoma with immunodeficiency). There was no significant difference between the age at diagnosis of the disorders, with the exception of XLA, with a median age at diagnosis of less than 1 year. The median age of reported symptom onset was 20 years for those with a diagnosis of CVID, with a median age at diagnosis of 35 years. CVID patients experienced significantly more non-infectious complications, such as autoimmune cytopenias and lymphoproliferative disease, than the other antibody deficiency disorders. The presence of non-infectious complications was associated with significantly reduced survival in the cohort.

© 2017 Elsevier Inc. Purpose: Many adults who stutter have elevated negative mood states like anxiety and depressive mood. Little is known about how mood states change over time. The purpose of this study was to determine the trajectories or sub-types of mood states in adults who stutter over a 6 month period, and establish factors that contribute to these sub-types. Method: Participants included 129 adults who stutter who completed an assessment regimen at baseline, including a measure of mood states (Symptom Checklist-90-Revised). Three mood states were assessed (interpersonal sensitivity or IS, depressive mood and anxiety) once a month over 6 months. Latent class growth mixture modeling was used to establish trajectories of change in these mood states over time. Logistic regression was then used to determine factors assessed at baseline that contribute to the IS trajectories. Results: Three-class trajectory models were accepted as the best fit for IS, depressive mood and anxiety mood sub-types. Stable and normal mood state sub-types were found, incorporating around 60% of participants. Up to 40% belonged to sub-types comprising elevated levels of negative mood states. The logistic regression was conducted only with the IS domain, and revealed four factors that significantly contributed to IS mood sub-types. Those with low perceived control, low vitality, elevated social fears and being female were more likely to belong to elevated IS classes. Conclusions: This research revealed mood sub-types in adults who stutter, providing direction for the treatment of stuttering. Clarification of how much stuttering influences mood sub-types versus pre-existing mood is required.

© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted. Objective To determine whether psychological distress associated with musculoskeletal injuries sustained in a motor vehicle crash (MVC), regardless of time of onset, impacts compensation outcomes such as claim settlement times and costs. Second, to identify factors routinely collected by insurance companies that contribute to psychological distress during the compensation process. Design Statewide retrospective study. Data source Analysis of the New South Wales statewide (Australia) injury register for MVC survivors who lodged a compensation claim from 2011 to 2013. Participants 6341 adults who sustained a musculoskeletal injury and who settled a claim for injury after an MVC. Participants included those diagnosed with psychological distress (n=607) versus those not (n=5734). Main outcome measures Time to settlement and total costs of claims, as well as socio-demographic and injury characteristics that may contribute to elevated psychological distress, such as socio-economic disadvantage, and injury severity. Results Psychological distress in those with a musculoskeletal injury was associated with significantly longer settlement times (an additional 17 weeks) and considerably higher costs (an additional $A41 575.00 or 4.3 times more expensive). Multivariate logistic regression analysis identified risk factors for psychological distress including being female, social disadvantage, unemployment prior to the claim, not being at fault in the MVC, requiring ambulance transportation and rehabilitation as part of recovery. Conclusions Results provide compelling evidence that psychological distress has an adverse impact on people with musculoskeletal injury as they progress through compensation. Findings suggest that additional resources should be directed toward claimants who are at risk

© 2017 Chai, Ling, San, Naik, Nguyen, Tran, Craig and Nguyen. This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively.

© 2016 IEEE. This paper presents a two-class electroencephal-ography-based classification for classifying of driver fatigue (fatigue state versus alert state) from 43 healthy participants. The system uses independent component by entropy rate bound minimization analysis (ERBM-ICA) for the source separation, autoregressive (AR) modeling for the features extraction, and Bayesian neural network for the classification algorithm. The classification results demonstrate a sensitivity of 89.7%, a specificity of 86.8%, and an accuracy of 88.2%. The combination of ERBM-ICA (source separator), AR (feature extractor), and Bayesian neural network (classifier) provides the best outcome with a p-value < 0.05 with the highest value of area under the receiver operating curve (AUC-ROC = 0.93) against other methods such as power spectral density as feature extractor (AUC-ROC = 0.81). The results of this study suggest the method could be utilized effectively for a countermeasure device for driver fatigue identification and other adverse event applications.

© 2017 IEEE. This paper presents a classification of driver fatigue with electroencephalography (EEG) channels selection analysis. The system employs independent component analysis (ICA) with scalp map back projection to select the dominant of EEG channels. After channel selection, the features of the selected EEG channels were extracted based on power spectral density (PSD), and then classified using a Bayesian neural network. The results of the ICA decomposition with the back-projected scalp map and a threshold showed that the EEG channels can be reduced from 32 channels into 16 dominants channels involved in fatigue assessment as chosen channels, which included AF3, F3, FC1, FC5, T7, CP5, P3, O1, P4, P8, CP6, T8, FC2, F8, AF4, FP2. The result of fatigue vs. alert classification of the selected 16 channels yielded a sensitivity of 76.8%, specificity of 74.3% and an accuracy of 75.5%. Also, the classification results of the selected 16 channels are comparable to those using the original 32 channels. So, the selected 16 channels is preferable for ergonomics improvement of EEG-based fatigue classification system.

© 2017 International Spinal Cord Society. Study design: A randomized controlled trial (RCT).Objectives: To determine the efficacy of massage therapy (MT) as a treatment that could be implemented to reduce pain and fatigue in people with chronic spinal cord injury (SCI).Setting: Laboratory setting in Sydney, Australia.Methods: Participants included 40 people with SCI living in the community who were randomly assigned into one of two RCT arms: MT (Swedish massage to upper body) or an active concurrent control (guided imagery (GI) relaxation). All participants received 30 min once a week of either massage or GI over 5 consecutive weeks. In addition to sociodemographic and injury factors, assessments and reliable measures including the short-form McGill Pain Questionnaire and Chalder's Fatigue Scale were validated.Results: Chronic pain and fatigue were significantly reduced in the massage group when assessed at the end of 5 weeks (P<0.05), with large effect sizes. Interestingly, GI was as effective as MT in reducing pain and fatigue. Pain scores were reduced significantly over time in both MT and GI groups (P=0.049 and P=0.032, respectively). Total fatigue scores were also reduced in both MT and GI groups (P=0004 and P=0.037, respectively.)Conclusions: Massage and GI are both active treatments that provide potential clinical benefits for adults with SCI. Future research should clarify the role of massage and GI in managing pain and fatigue in SCI and assess outcomes into the longer-term.