466 resultados para Glasgow Outcome Scale
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Despite being used since 1976, Delusions-Symptoms-States-Inventory/states of Anxiety and Depression (DSSI/sAD) has not yet been validated for use among people with diabetes. The aim of this study was to examine the validity of the personal disturbance scale (DSSI/sAD) among women with diabetes using Mater-University of Queensland Study of Pregnancy (MUSP) cohort data. The DSSI subscales were compared against DSM-IV disorders, the Mental Component Score of the Short Form 36 (SF-36 MCS), and Center for Epidemiologic Studies Depression Scale (CES-D). Factor analyses, odds ratios, receiver operating characteristic (ROC) analyses and diagnostic efficiency tests were used to report findings. Exploratory factor analysis and fit indices confirmed the hypothesized two-factor model of DSSI/sAD. We found significant variations in the DSSI/sAD domain scores that could be explained by CES-D (DSSI-Anxiety: 55%, DSSI-Depression: 46%) and SF-36 MCS (DSSI-Anxiety: 66%, DSSI-Depression: 56%). The DSSI subscales predicted DSM-IV diagnosed depression and anxiety disorders. The ROC analyses show that although the DSSI symptoms and DSM-IV disorders were measured concurrently the estimates of concordance remained only moderate. The findings demonstrate that the DSSI/sAD items have similar relationships to one another in both the diabetes and non-diabetes data sets which therefore suggest that they have similar interpretations.
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PURPOSE: This paper describes dynamic agent composition, used to support the development of flexible and extensible large-scale agent-based models (ABMs). This approach was motivated by a need to extend and modify, with ease, an ABM with an underlying networked structure as more information becomes available. Flexibility was also sought after so that simulations are set up with ease, without the need to program. METHODS: The dynamic agent composition approach consists in having agents, whose implementation has been broken into atomic units, come together at runtime to form the complex system representation on which simulations are run. These components capture information at a fine level of detail and provide a vast range of combinations and options for a modeller to create ABMs. RESULTS: A description of the dynamic agent composition is given in this paper, as well as details about its implementation within MODAM (MODular Agent-based Model), a software framework which is applied to the planning of the electricity distribution network. Illustrations of the implementation of the dynamic agent composition are consequently given for that domain throughout the paper. It is however expected that this approach will be beneficial to other problem domains, especially those with a networked structure, such as water or gas networks. CONCLUSIONS: Dynamic agent composition has many advantages over the way agent-based models are traditionally built for the users, the developers, as well as for agent-based modelling as a scientific approach. Developers can extend the model without the need to access or modify previously written code; they can develop groups of entities independently and add them to those already defined to extend the model. Users can mix-and-match already implemented components to form large-scales ABMs, allowing them to quickly setup simulations and easily compare scenarios without the need to program. The dynamic agent composition provides a natural simulation space over which ABMs of networked structures are represented, facilitating their implementation; and verification and validation of models is facilitated by quickly setting up alternative simulations.
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Epigenetic changes correspond to heritable modifications of the chromosome structure, which do not involve alteration of the DNA sequence but do affect gene expression. These mechanisms play an important role in normal cell differentiation, but aberration is associated also with several diseases, including cancer and neural disorders. In consequence, despite intensive studies in recent years, the contribution of modifications remains largely unquantified due to overall system complexity and insufficient data. Computational models can provide powerful auxiliary tools to experimentation, not least as scales from the sub-cellular through cell populations (or to networks of genes) can be spanned. In this paper, the challenges to development, of realistic cross-scale models, are discussed and illustrated with respect to current work.
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This study shows that there is positive regulatory effect of feedback from pupils to teachers on Assessment for Learning (AfL), classroom proactiveness, and on visible and progressive learning but not on behaviour. This research finding further articulates feedback from pupil to teacher as a paradigm shift from the classical paradigm of feedback from teacher to pupil. Here, the emphasis is geared towards pupils understanding of objectives built from previous knowledge. These are then feedback onto the teachers by the pupils in the form of discrete loops of cues and questions, where they are with their learning. This therefore enables them to move to the next level of understanding, and thus acquired independence, which in turn is reflected by their success in both formative and summative assessments. This study therefore shows that when feedback from pupil to teacher is used in combination with teacher to pupil feedback, AfL is ameliorated and hence, visible and accelerated learning occurs in a gender, nor subject non-dependent manner.
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The Queensland Department of Transport and Main Roads (TMR) required an evaluation framework for the Queensland Alcohol Ignition Interlock Program (AIIP). The objective of this project was to develop a framework to evaluate the AIIP in terms of its effect on road safety outcomes.
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There is an increasing need in biology and clinical medicine to robustly and reliably measure tens-to-hundreds of peptides and proteins in clinical and biological samples with high sensitivity, specificity, reproducibility and repeatability. Previously, we demonstrated that LC-MRM-MS with isotope dilution has suitable performance for quantitative measurements of small numbers of relatively abundant proteins in human plasma, and that the resulting assays can be transferred across laboratories while maintaining high reproducibility and quantitative precision. Here we significantly extend that earlier work, demonstrating that 11 laboratories using 14 LC-MS systems can develop, determine analytical figures of merit, and apply highly multiplexed MRM-MS assays targeting 125 peptides derived from 27 cancer-relevant proteins and 7 control proteins to precisely and reproducibly measure the analytes in human plasma. To ensure consistent generation of high quality data we incorporated a system suitability protocol (SSP) into our experimental design. The SSP enabled real-time monitoring of LC-MRM-MS performance during assay development and implementation, facilitating early detection and correction of chromatographic and instrumental problems. Low to sub-nanogram/mL sensitivity for proteins in plasma was achieved by one-step immunoaffinity depletion of 14 abundant plasma proteins prior to analysis. Median intra- and inter-laboratory reproducibility was <20%, sufficient for most biological studies and candidate protein biomarker verification. Digestion recovery of peptides was assessed and quantitative accuracy improved using heavy isotope labeled versions of the proteins as internal standards. Using the highly multiplexed assay, participating laboratories were able to precisely and reproducibly determine the levels of a series of analytes in blinded samples used to simulate an inter-laboratory clinical study of patient samples. Our study further establishes that LC-MRM-MS using stable isotope dilution, with appropriate attention to analytical validation and appropriate quality c`ontrol measures, enables sensitive, specific, reproducible and quantitative measurements of proteins and peptides in complex biological matrices such as plasma.
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Pilot and industrial scale dilute acid pretreatment data can be difficult to obtain due to the significant infrastructure investment required. Consequently, models of dilute acid pretreatment by necessity use laboratory scale data to determine kinetic parameters and make predictions about optimal pretreatment conditions at larger scales. In order for these recommendations to be meaningful, the ability of laboratory scale models to predict pilot and industrial scale yields must be investigated. A mathematical model of the dilute acid pretreatment of sugarcane bagasse has previously been developed by the authors. This model was able to successfully reproduce the experimental yields of xylose and short chain xylooligomers obtained at the laboratory scale. In this paper, the ability of the model to reproduce pilot scale yield and composition data is examined. It was found that in general the model over predicted the pilot scale reactor yields by a significant margin. Models that appear very promising at the laboratory scale may have limitations when predicting yields on a pilot or industrial scale. It is difficult to comment whether there are any consistent trends in optimal operating conditions between reactor scale and laboratory scale hydrolysis due to the limited reactor datasets available. Further investigation is needed to determine whether the model has some efficacy when the kinetic parameters are re-evaluated by parameter fitting to reactor scale data, however, this requires the compilation of larger datasets. Alternatively, laboratory scale mathematical models may have enhanced utility for predicting larger scale reactor performance if bulk mass transport and fluid flow considerations are incorporated into the fibre scale equations. This work reinforces the need for appropriate attention to be paid to pilot scale experimental development when moving from laboratory to pilot and industrial scales for new technologies.
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Background: Individuals who fear falling may restrict themselves from performing certain activities and may increase their risk of falling. Such fear, reflected in the form of falls efficacy, has been measured in only a small number of studies measuring the effectiveness of exercise interventions in the elderly. This may be due to the various types of exercise that can be performed. Hence the effectiveness of exercise on falls efficacy is relatively understudied. Therefore, there is a need to measure falls efficacy as an outcome variable when conducting exercise interventions in the elderly. Methods: A total of 43 elderly community-dwelling volunteers were recruited and randomly allocated to a conventional exercise intervention, a holistic exercise intervention, or a control group. The interventions were performed 2 days per week for 10 weeks. Falls efficacy was measured at baseline and at the completion of the interventions using the Modified Falls Efficacy Scale (MFES). Results: Within group comparisons between baseline and follow-up indicated no significant improvements in falls efficacy, however, the difference for the conventional exercise group approached statistical significance (baseline 8.9 to follow-up 9.3; P = 0.058). Within group comparisons of mean difference MFES scores showed a significant difference between the conventional exercise group and the control group (conventional exercise group 0.4 vs control group −0.6; P < 0.05). Conclusion: Given the lack of significant improvements in falls efficacy found for any of the groups, it cannot be concluded whether a conventional or a holistic exercise intervention is the best approach for improving falls efficacy. It is possible that the characteristics of the exercise interventions including specificity, intensity, frequency and duration need to be manipulated if the purpose is to bring about improvements in falls efficacy.
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PURPOSE To review records of 330 patients who underwent surgery for femoral neck fractures with or without preoperative anticoagulation therapy. METHODS Medical records of 235 women and 95 men aged 48 to 103 years (mean, 81.6; standard deviation [SD], 13.1) who underwent surgery for femoral neck fractures with or without preoperative anticoagulation therapy were reviewed. 30 patients were on warfarin, 105 on aspirin, 28 on clopidogrel, and 167 were controls. The latter 3 groups were combined as the non-warfarin group and compared with the warfarin group. Hospital mortality, time from admission to surgery, length of hospital stay, return to theatre, and postoperative complications (wound infection, deep vein thrombosis, and pulmonary embolism) were assessed. RESULTS The warfarin and control groups were significantly younger than the clopidogrel and aspirin groups (80.8 vs. 80.0 vs. 84.2 vs. 83.7 years, respectively, p<0.05). 81% of the patients underwent surgery within 48 hours of admission. The overall mean time from admission to surgery was 1.8 days; it was longer in the warfarin than the aspirin, clopidogrel, and control groups (3.3 vs. 1.8 vs. 1.6 vs. 1.6 days, respectively, p<0.001). The mean length of hospital stay was 17.5 (SD, 9.6; range, 3-54) days. The overall hospital mortality was 3.9%; it was 6.7% in the warfarin group, 3.8% in the aspirin group, 3.6% in the clopidogrel group, and 3.6% in the control group (p=0.80). Four patients returned to theatre for surgery: one in the warfarin group for washout of a haematoma, 2 in the aspirin group for repositioning of a mal-fixation and for debridement of wound infection, and one in the control group for debridement of wound infection. The warfarin group did not differ significantly from non-warfarin group in terms of postoperative complication rate (6.7% vs. 2.7%, p=0.228) and the rate of return to theatre (3.3% vs. 1%, p=0.318). CONCLUSION It is safe to continue aspirin and clopidogrel prior to surgical treatment for femoral neck fracture. The risk of delaying surgery outweighs the peri-operative bleeding risk.
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Purpose This study tested the effectiveness of a pressure ulcer (PU) prevention bundle in reducing the incidence of PUs in critically ill patients in two Saudi intensive care units (ICUs). Design A two-arm cluster randomized experimental control trial. Methods Participants in the intervention group received the PU prevention bundle, while the control group received standard skin care as per the local ICU policies. Data collected included demographic variables (age, diagnosis, comorbidities, admission trajectory, length of stay) and clinical variables (Braden Scale score, severity of organ function score, mechanical ventilation, PU presence, and staging). All patients were followed every two days from admission through to discharge, death, or up to a maximum of 28 days. Data were analyzed with descriptive correlation statistics, Kaplan-Meier survival analysis, and Poisson regression. Findings The total number of participants recruited was 140: 70 control participants (with a total of 728 days of observation) and 70 intervention participants (784 days of observation). PU cumulative incidence was significantly lower in the intervention group (7.14%) compared to the control group (32.86%). Poisson regression revealed the likelihood of PU development was 70% lower in the intervention group. The intervention group had significantly less Stage I (p = 002) and Stage II PU development (p = 026). Conclusions Significant improvements were observed in PU-related outcomes with the implementation of the PU prevention bundle in the ICU; PU incidence, severity, and total number of PUs per patient were reduced. Clinical Relevance Utilizing a bundle approach and standardized nursing language through skin assessment and translation of the knowledge to practice has the potential to impact positively on the quality of care and patient outcome.
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The proliferation of the web presents an unsolved problem of automatically analyzing billions of pages of natural language. We introduce a scalable algorithm that clusters hundreds of millions of web pages into hundreds of thousands of clusters. It does this on a single mid-range machine using efficient algorithms and compressed document representations. It is applied to two web-scale crawls covering tens of terabytes. ClueWeb09 and ClueWeb12 contain 500 and 733 million web pages and were clustered into 500,000 to 700,000 clusters. To the best of our knowledge, such fine grained clustering has not been previously demonstrated. Previous approaches clustered a sample that limits the maximum number of discoverable clusters. The proposed EM-tree algorithm uses the entire collection in clustering and produces several orders of magnitude more clusters than the existing algorithms. Fine grained clustering is necessary for meaningful clustering in massive collections where the number of distinct topics grows linearly with collection size. These fine-grained clusters show an improved cluster quality when assessed with two novel evaluations using ad hoc search relevance judgments and spam classifications for external validation. These evaluations solve the problem of assessing the quality of clusters where categorical labeling is unavailable and unfeasible.
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BACKGROUND: An early response to antipsychotic treatment in patients with psychosis has been associated with a better course and outcome. However, factors that predict treatment response are not well understood. The onset of schizophrenia and related disorders has been associated with increased levels of stress and hyper-activation of the hypothalamic-pituitary-adrenal (HPA) axis. This study examined whether pituitary volume at the onset of psychosis may be a potential predictor of early treatment response in first-episode psychosis (FEP) patients. METHODS: We investigated the relationship between baseline pituitary volume and symptomatic treatment response over 12 weeks using mixed model analysis in a sample of 42 drug-naïve or early treated FEP patients who participated in a controlled dose-finding study of quetiapine fumarate. Logistic regression was used to examine predictors of treatment response. Pituitary volume was measured from magnetic resonance imaging scans that were obtained upon entry into the trial. RESULTS: Larger pituitary volume was associated with less improvement in overall psychotic symptoms (Brief Psychiatric Rating Scale (BPRS) P=0.031) and positive symptoms (BPRS positive symptom subscale P=0.010). Regardless of gender, patients with a pituitary volume at the 25th percentile (413 mm(3)) were approximately three times more likely to respond to treatment by week 12 than those at the 75th percentile (635 mm(3)) (odds ratio=3.07, CI: 0.90-10.48). CONCLUSION: The association of baseline pituitary volumes with early treatment response highlights the importance of the HPA axis in emerging psychosis. Potential implications for treatment strategies in early psychosis are discussed.
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Background The use of mobile apps for health and well being promotion has grown exponentially in recent years. Yet, there is currently no app-quality assessment tool beyond “star”-ratings. Objective The objective of this study was to develop a reliable, multidimensional measure for trialling, classifying, and rating the quality of mobile health apps. Methods A literature search was conducted to identify articles containing explicit Web or app quality rating criteria published between January 2000 and January 2013. Existing criteria for the assessment of app quality were categorized by an expert panel to develop the new Mobile App Rating Scale (MARS) subscales, items, descriptors, and anchors. There were sixty well being apps that were randomly selected using an iTunes search for MARS rating. There were ten that were used to pilot the rating procedure, and the remaining 50 provided data on interrater reliability. Results There were 372 explicit criteria for assessing Web or app quality that were extracted from 25 published papers, conference proceedings, and Internet resources. There were five broad categories of criteria that were identified including four objective quality scales: engagement, functionality, aesthetics, and information quality; and one subjective quality scale; which were refined into the 23-item MARS. The MARS demonstrated excellent internal consistency (alpha = .90) and interrater reliability intraclass correlation coefficient (ICC = .79). Conclusions The MARS is a simple, objective, and reliable tool for classifying and assessing the quality of mobile health apps. It can also be used to provide a checklist for the design and development of new high quality health apps.
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Background Depression is a common psychiatric disorder in older people. The study aimed to examine the screening accuracy of the Geriatric Depression Scale (GDS) and the Collateral Source version of the Geriatric Depression Scale (CS-GDS) in the nursing home setting. Methods Eighty-eight residents from 14 nursing homes were assessed for depression using the GDS and the CS-GDS, and validated against clinician diagnosed depression using the Semi-structured Clinical Diagnostic Interview for DSM-IV-TR Axis I Disorders (SCID) for residents without dementia and the Provisional Diagnostic Criteria for Depression in Alzheimer Disease (PDCdAD) for those with dementia. The screening performances of five versions of the GDS (30-, 15-, 10-, 8-, and 4-item) and two versions of the CS-GDS (30- and 15-item) were analyzed using receiver operating characteristic (ROC) curves. Results Among residents without dementia, both the self-rated (AUC = 0.75–0.79) and proxy-rated (AUC = 0.67) GDS variations performed significantly better than chance in screening for depression. However, neither instrument adequately identified depression among residents with dementia (AUC between 0.57 and 0.70). Among the GDS variations, the 4- and 8-item scales had the highest AUC and the optimal cut-offs were >0 and >3, respectively. Conclusions The validity of the GDS in detecting depression requires a certain level of cognitive functioning. While the CS-GDS is designed to remedy this issue by using an informant, it did not have adequate validity in detecting depression among residents with dementia. Further research is needed on informant selection and other factors that can potentially influence the validity of proxy-based measures in the nursing home setting.
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Existing field data for Rangal coals (Late Permian) of the Bowen Basin, Queensland, Australia, are inconsistent with the depositional model generally accepted in the current geological literature to explain coal deposition. Given the apparent unsuitability of the current depositional model to the Bowen Basin coal data, a new depositional model, here named the Cyclic Salinity Model, is proposed and tested in this study.