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Basic color terms used in Mandarin Chinese have been controversial since first discussed by Berlin and Kay in 1969. Previous studies showed much inconsistency on what should be considered as basic color terms in Mandarin Chinese. In the present study, we investigated categories of color rather than merely the color terms used by Taiwanese native Mandarin speakers. Using samples conforming to the Berlin and Kay survey, various colors were chosen from a collection of Natural Color System (NCS) colored papers and mounted on a piece of neutral gray card. The card was then mounted on a touch-screen, under D65 illumination. Thirty-two single-character color related Mandarin terms were selected from a Chinese character database according to frequency of use. Participants were required to select the color sample that matched the term by pressing a virtual button on the touch screen. The results show that certain terms can be directly correlated to basic color terms in English, comparable with the results of Berlin and Kay’s original study and those that followed. However, some terms, such as Mo (墨 ink), Tie (鐵 iron), and Cai (菜vegetable), show a wide spread of term maps and inconsistent use among subjects. Principle component analysis (PCA) procedures were used to analysis the commodity of data among subjects. The findings suggest that the basic color categories among Mandarin Chinese speakers are similar to those found in the World Color Survey (WCS), but are represented by wide-spread and inconsistent color terms among speakers. [ABSTRACT FROM AUTHOR]

ICD-10(International Classification of Diseases 10th revision) is a classification of a disease, symptom, procedure, or injury. Diseases are often described in patients’ medical records with free texts, such as terms, phrases and paraphrases, which differ significantly from those used in ICD-10 classification. This paper presents an improved approach based on the Longest Common Subsequence (LCS) and semantic similarity for automatic Chinese diagnoses, mapping from the disease names given by clinician to the disease names in ICD-10. LCS refers to the longest string that is a subsequence of every member of a given set of strings. The proposed method of improved LCS in this paper can increase the accuracy of processing in Chinese disease mapping. [ABSTRACT FROM AUTHOR]

In sports leagues that use an annual draft to assign eligible players to clubs, having a value associated with a draft selection can allow clubs to anticipate future growth of players and, if a trading period exists, assist negotiations when exchanging draft selections and players. Typically, mean draft values often decline in either an exponential or geometric manner with increasing draft selection number. Aggregate mean values have been used to compare trade packages. However, clubs may also want to ensure that a trade does not increase the probability of obtaining poor players in the draft. This paper therefore considers equivalence of risk as an alternative trading strategy for club list managers. Here, risk is defined as the probability of the aggregate value of the received draft selections being below a minimum acceptable level. For risk equivalence, a premium over and above mean market value may need to be provided when trading to secure higher draft selections. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) is widely used in maximum power point tracking (MPPT) of photovoltaic (PV) energy systems. Nevertheless, this technique suffers from two main problems in the case of partial shading conditions (PSCs). The first problem is that PSO is a time invariant optimization technique that cannot follow the dynamic global peak (GP) under time variant shading patterns (SPs) and sticks to the first GP that occurs at the beginning. This problem can be solved by dispersing the PSO particles using two new techniques introduced in this paper. The two new proposed PSO re-initialization techniques are to disperse the particles upon the SP changes and the other one is upon a predefined time (PDT). The second problem is regarding the high oscillations around steady state, which can be solved by using fuzzy logic controller (FLC) to fine-tune the output power and voltage from the PV system. The new contribution of this paper is the hybrid PSO-FLC with two PSO particles dispersing techniques that is able to solve the two previous mentioned problems effectively and improve the performance of the PV system in both normal and PSCs. A detailed list of comparisons between hybrid PSO-FLC and original PSO using the two proposed methodologies are achieved. The results prove the superior performance of hybrid PSO-FLC compared to PSO in terms of efficiency, accuracy, oscillations reduction around steady state and soft tuning of the GP tracked. [ABSTRACT FROM AUTHOR]

In business replenishment, discount offers of multi-item may either provide different discount schedules with a single discount type, or provide schedules with multiple discount types. The paper investigates the joint effects of multiple discount schemes on the decisions of multi-item joint replenishment. In this paper, a joint replenishment problem (JRP) model, considering three discount (all-unit discount, incremental discount, total volume discount) offers simultaneously, is constructed to determine the basic cycle time and joint replenishment frequencies of multi-item. To solve the proposed problem, a heuristic algorithm is proposed to find the optimal solutions and the corresponding total cost of the JRP model. Numerical experiment is performed to test the algorithm and the computational results of JRPs under different discount combinations show different significance in the replenishment cost reduction. [ABSTRACT FROM AUTHOR]

Background: Diabetes is a chronic disease commonly managed by family physicians, with the most prevalent complication being cardiovascular disease (CVD). Clinical practice guidelines have been developed to support clinicians in the care of diabetic patients. We conducted a pragmatic cluster randomized controlled trial (RCT) of a printed educational toolkit aimed at improving CVD management in diabetes in primary care, and found no effect, and indeed, the possibility of some harm. We conducted a qualitative evaluation to study the strategy for guideline implementation employed in this trial, and to understand its effects. This paper focuses solely on the qualitative findings, as the RCT’s quantitative results have already been reported elsewhere. Methods and Findings: All family practices in the province of Ontario had been randomized to receive the educational toolkit by mail, in either the summer of 2009 (intervention arm) or the spring of 2010 (control arm).A subset of 80 family physicians (representing approximately 10% of the practices randomized and approached, with records on 1,592 randomly selected patients with diabetes at high risk for CVD) then took part in a chart audit and reflective feedback exercise related to their own practice in comparison to the guideline recommendations. They were asked to complete two forms (one pre- and one post-audit) in order to understand their awareness of the guidelines pre-trial, their expectations regarding their individual performance pre-audit, and their reflections on their audit results. In addition, individual interviews with thirteen other family physicians were conducted. Textual data from interview transcripts and written commentary from the pre- and post-audit forms underwent qualitative descriptive analysis to identify common themes and patterns. Analysis revealed four main themes: impressions of the toolkit, awareness was not the issue, ‘it’s not me it’s my patients’, and chart audit as a more effective intervention than the toolkit. Participants saw neither the toolkit content nor its dissemination strategy to be effective, indicating they perceived themselves to be aware of the guidelines pre-trial. However, their accounts also indicated that they may be struggling to prioritize CVD management in the midst of competing demands for their attention. Upon receiving their chart audit results, many participants expressed surprise that they had not performed better. They reported that the audit results would be an important motivator for behaviour change. Conclusions: The qualitative findings outlined in this paper offer important insights into why the intervention was not effective. They also demonstrate that physicians have unperceived needs relative to CVD management and that the chart audit served to identify shortcomings in their practice of which they had been hitherto unaware. The findings also indicate that new methods of intervention development and implementation should be explored. This is important given the high prevalence of diabetes worldwide; appropriate CVD management is critical to addressing the morbidity and mortality associated with the disease. [ABSTRACT FROM AUTHOR]

Background: System science approaches are increasingly used to explore complex public health problems. Quantitative methods, such as participatory dynamic simulation modelling, can mobilise knowledge to inform health policy decisions. However, the analytic and practical steps required to turn collaboratively developed, qualitative system maps into rigorous and policy-relevant quantified dynamic simulation models are not well described. This paper reports on the processes, interactions and decisions that occurred at the interface between modellers and end-user participants in an applied health sector case study focusing on diabetes in pregnancy. Methods: An analysis was conducted using qualitative data from a participatory dynamic simulation modelling case study in an Australian health policy setting. Recordings of participatory model development workshops and subsequent meetings were analysed and triangulated with field notes and other written records of discussions and decisions. Case study vignettes were collated to illustrate the deliberations and decisions made throughout the model development process. Results: The key analytic objectives and decision-making processes included: defining the model scope; analysing and refining the model structure to maximise local relevance and utility; reviewing and incorporating evidence to inform model parameters and assumptions; focusing the model on priority policy questions; communicating results and applying the models to policy processes. These stages did not occur sequentially; the model development was cyclical and iterative with decisions being re-visited and refined throughout the process. Storytelling was an effective strategy to both communicate and resolve concerns about the model logic and structure, and to communicate the outputs of the model to a broader audience. Conclusion: The in-depth analysis reported here examined the application of participatory modelling methods to move beyond qualitative conceptual mapping to the development of a rigorously quantified and policy relevant, complex dynamic simulation model. The analytic objectives and decision-making themes identified provide guidance for interpreting, understanding and reporting future participatory modelling projects and methods. [ABSTRACT FROM AUTHOR]

Introduction: Several versions of Early Warning Systems (EWS) are used in obstetrics to detect and treat early clinical deterioration to avert morbidity and mortality. EWS can potentially be useful to improve the quality of care and reduce the risk of maternal mortality in resource-limited settings. We conducted a systematic literature review of published obstetric early warning systems, define their predictive accuracy for morbidity and mortality, and their effectiveness in triggering corrective actions and improving health outcomes. Methods: We systematically searched for primary research articles on obstetric EWS published in peer-reviewed journals between January 1997 and March 2018 in Medline, CINAHL, SCOPUS, Science Direct, and Science Citation Index. We also searched reference lists of relevant articles and websites of professional societies. We included studies that assessed the predictive accuracy of EWS to detect clinical deterioration, or/and their effectiveness in improving clinical outcomes in obstetric inpatients. We excluded studies with a paediatric or non-obstetric adult population. Cross-sectional and qualitative studies were also excluded. We performed a narrative synthesis since the outcomes reported were heterogeneous. Results: A total of 381 papers were identified, 17 of which met the inclusion criteria. Eleven of the included studies evaluated the predictive accuracy of EWS for obstetric morbidity and mortality, 5 studies assessed the effectiveness of EWS in improving clinical outcomes, while one study addressed both. Sixteen published EWS versions were reviewed, 14 of which included five basic clinical observations (pulse rate, respiratory rate, temperature, blood pressure, and consciousness level). The obstetric EWS identified had very high median (inter-quartile range) sensitivity—89% (72% to 97%) and specificity—85% (67% to 98%) but low median (inter-quartile range) positive predictive values—41% (25% to 74%) for predicting morbidity or ICU admission. Obstetric EWS had a very high accuracy in predicting death (AUROC >0.80) among critically ill obstetric patients. Obstetric EWS improves the frequency of routine vital sign observation, reduces the interval between the recording of specifically defined abnormal clinical observations and corrective clinical actions, and can potentially reduce the severity of obstetric morbidity. Conclusion: Obstetric EWS are effective in predicting severe morbidity (in general obstetric population) and mortality (in critically ill obstetric patients). EWS can contribute to improved quality of care, prevent progressive obstetric morbidity and improve health outcomes. There is limited evidence of the effectiveness of EWS in reducing maternal death across all settings. Clinical parameters in most obstetric EWS versions are routinely collected in resource-limited settings, therefore implementing EWS may be feasible in such settings. [ABSTRACT FROM AUTHOR]

Background: BMI is a feasible and recommended measure for overweight and obesity screening in children and adolescents. The study aimed to determine how often physicians correctly identified obesity/ overweight status in children and adolescents by using BMI percentile charts. Methods: This retrospective cross-sectional study reviewed the paper medical records of children and adolescents (6–14 years) who visited family medicine and pediatric outpatient clinics (Jan-June 2012) in a medical city in Riyadh. Investigators calculated BMI percentiles (using height, weight, age and gender data retrieved from the records) in order to identify patient weight status. Physician documentation of obesity/overweight diagnoses in patient problem lists were cross checked against their BMI percentile to assess the accuracy of physicians’ identification of weight status. The recommended management plan for identified patients was also recorded. Results: A total of 481 charts were reviewed, 213 (44%) children were seen by family medicine physicians and 268 (56%) by pediatricians. The sample was equally distributed by gender. Height was undocumented for 13% (71) of visiting patients. Eighteen percent of patients (86) were classified as overweight (35)/obese (51) according to age and sex adjusted BMI percentile. Physicians’ correctly identified and documented weight status in 20% of overweight/obese patients: 17 out of 86 subjects. Weight status identification was higher among pediatricians—25% as compared to family medicine physicians—10% [p = 0.08]. Dietary referral was the most common management plan for the identified children. Physicians were more likely to identify obese children {≥95th} compared to overweight {≥85th - 95th} children. Subjects whose BMI for age classified them into the highest BMI percentile category {≥95th} were more likely to be correctly identified (29%) compared to those classified within {≥85th - 95th} category—6% [p = 0.007]. Conclusion: Physician identification of obesity/ overweight status for children and adolescents was low, irrespective of their specialty, and despite the condition being prevalent in the sample. Future research that concentrates on interventions that may improve documentation of obesity/overweight diagnoses and parameters needed for BMI indices would be beneficial. [ABSTRACT FROM AUTHOR]

The current marketing landscape, apart from conventional approaches, consists of campaigns designed especially for launching information diffusion processes within online networks. Associated research is focused on information propagation models, campaign initialization strategies and factors affecting campaign dynamics. In terms of algorithms and performance evaluation, the final coverage represented by the fraction of activated nodes within a target network is usually used. It is not necessarily consistent with the real marketing campaigns using various characteristics and parameters related to coverage, costs, behavioral patterns and time factors for overall evaluation. This paper presents assumptions for a decision support system for multi-criteria campaign planning and evaluation with inputs from agent-based simulations. The results, which are delivered from a simulation model based on synthetic networks in a form of decision scenarios, are verified within a real network. Last, but not least, the study proposes a multi-objective campaign evaluation framework with several campaign evaluation metrics integrated. The results showed that the recommendations generated with the use of synthetic networks applied to real networks delivered results according to the decision makers’ expectation in terms of the used evaluation criteria. Apart from practical applications, the proposed multi-objective approach creates new evaluation possibilities for theoretical studies focused on information spreading processes within complex networks. [ABSTRACT FROM AUTHOR]

In this paper, a two-stage filter for removing salt-and-pepper noise using noise detector based on characteristic difference parameter and adaptive directional mean filter is proposed. The first stage firstly detects the noise corrupted pixels by combining characteristic difference parameter and gray level extreme, then develops an improved adaptive median filter to firstly restore them. The second stage introduces a restoration scheme to further restore the noise corrupted pixels, which firstly divides them into two types and then applies different restoration skills for the pixels based on the classification result. One type of pixels is restored by the mean filter and the other type of pixels is restored by the proposed adaptive directional mean filter. The new filter firstly adaptively selects the optimal filtering window and direction template, then replaces the gray level of noise corrupted pixel by the mean value of pixels on the optimal template. Experimental results show that the proposed filter outperforms many existing main filters in terms of noise suppression and detail preservation. [ABSTRACT FROM AUTHOR]

Introduction: This study systematically reviews costing studies of seasonal influenza-like illness (ILI) in high-income countries. Existing reviews on the economic impact of ILI do not report information on drug consumption and its costs, nor do they provide data on the overall cost per episode. Methods: The PRISMA-P checklist was used to design the research protocol. Studies included were cost of illness analysis (COI) and modeling studies that estimated the cost of ILI episodes. Records were searched from January 2000 to December 2016 in electronic bibliographic databases including Medline, Embase, Science Direct, the Cochrane Library, the Centre for Reviews and Disseminations of the University of York, and Google scholar. References from the included studies were hand-searched for completion. Abstract screening, full-text analysis and data extraction were performed by two reviewers independently and discrepancies were resolved by discussion with a third reviewer. A standardized, pre-piloted form was used for data extraction. All costs were converted to 2015 US$ Purchasing Power Parities. Results: The literature search identified 5,104 records. After abstract and title screening, 76 studies were analyzed full-text and 27 studies were finally included in the review. Full estimates of the cost per episode range from US$19 in Korea to US$323 in Germany. Particularly, the cost per episode of laboratory confirmed influenza cases was estimated between US$64 and US$73. Inpatient and outpatient services account for the majority of the costs. Differences in the estimates may reflect country-specific characteristics, as well as other study-specific features including study design, identification strategy of ILI cases, study populations and types of costs included in the analysis. Children usually register higher costs, whereas evidence for the elderly is less conclusive. Patients risk-profile, co-morbidities and complications are the other important cost-drivers. None of the papers considered appropriateness in resource use (e.g. abuse of antibiotics). Despite cost of illness studies have ultimately a descriptive role, evidence on (in)appropriateness is useful for policy-makers. [ABSTRACT FROM AUTHOR]

In this paper, ranked set sampling is used for developing a non-parametric location chart which is developed on the basis of Wilcoxon signed rank statistic. The average run length and some other characteristics of run length are used as the measures to assess the performance of the proposed scheme. Some selective distributions including Laplace (or double exponential), logistic, normal, contaminated normal and student’s t-distributions are considered to examine the performance of the proposed Wilcoxon signed rank control chart. It has been observed that the proposed scheme shows superior shift detection ability than some of the competing counterpart schemes covered in this study. Moreover, the proposed control chart is also implemented and illustrated with a real data set. [ABSTRACT FROM AUTHOR]

In secondary analysis of electronic health records, a crucial task consists in correctly identifying the patient cohort under investigation. In many cases, the most valuable and relevant information for an accurate classification of medical conditions exist only in clinical narratives. Therefore, it is necessary to use natural language processing (NLP) techniques to extract and evaluate these narratives. The most commonly used approach to this problem relies on extracting a number of clinician-defined medical concepts from text and using machine learning techniques to identify whether a particular patient has a certain condition. However, recent advances in deep learning and NLP enable models to learn a rich representation of (medical) language. Convolutional neural networks (CNN) for text classification can augment the existing techniques by leveraging the representation of language to learn which phrases in a text are relevant for a given medical condition. In this work, we compare concept extraction based methods with CNNs and other commonly used models in NLP in ten phenotyping tasks using 1,610 discharge summaries from the MIMIC-III database. We show that CNNs outperform concept extraction based methods in almost all of the tasks, with an improvement in F1-score of up to 26 and up to 7 percentage points in area under the ROC curve (AUC). We additionally assess the interpretability of both approaches by presenting and evaluating methods that calculate and extract the most salient phrases for a prediction. The results indicate that CNNs are a valid alternative to existing approaches in patient phenotyping and cohort identification, and should be further investigated. Moreover, the deep learning approach presented in this paper can be used to assist clinicians during chart review or support the extraction of billing codes from text by identifying and highlighting relevant phrases for various medical conditions. [ABSTRACT FROM AUTHOR]

This paper reports the dynamical analysis of the business cycles of 12 (developed and developing) countries over the last 56 years by applying computational techniques used for tackling complex systems. They reveal long-term convergence and country-level interconnections because of close contagion effects caused by bilateral networking exposure. Interconnectivity determines the magnitude of cross-border impacts. Local features and shock propagation complexity also may be true engines for local configuration of cycles. The algorithmic modeling proves to represent a solid approach to study the complex dynamics involved in the world economies. [ABSTRACT FROM AUTHOR]

With more and more political candidates using social media for campaigning, researchers are looking at measuring the effectiveness of this medium. Most research, however, concentrates on the bare count of likes (or twitter mentions) in an attempt to correlate social media presence and winning. In this paper, we propose a novel method, Interaction Strength Plot (IntS) to measure the passive interactions between a candidate’s posts on Facebook and the users (liking the posts). Using this method on original Malaysian General Election (MGE13) and Australian Federal Elections (AFE13) Facebook Pages (FP) campaign data, we label an FP as performing well if both the posting frequency and the likes gathered are above average. Our method shows that over 60% of the MGE13 candidates and 85% of the AFE13 candidates studied in this paper had under-performing FP. Some of these FP owners would have been identified as popular based on bare count. Thus our performance chart is a vital step forward in measuring the effectiveness of online campaigning. [ABSTRACT FROM AUTHOR]

Cone penetration testing (CPT) is one of the most efficient and versatile methods currently available for geotechnical, lithostratigraphic and hydrogeological site characterization. Currently available methods for soil behaviour type classification (SBT) of CPT data however have severe limitations, often restricting their application to a local scale. For parameterization of regional groundwater flow or geotechnical models, and delineation of regional hydro- or lithostratigraphy, regional SBT classification would be very useful. This paper investigates the use of model-based clustering for SBT classification, and the influence of different clustering approaches on the properties and spatial distribution of the obtained soil classes. We additionally propose a methodology for automated lithostratigraphic mapping of regionally occurring sedimentary units using SBT classification. The methodology is applied to a large CPT dataset, covering a groundwater basin of ~60 km2 with predominantly unconsolidated sandy sediments in northern Belgium. Results show that the model-based approach is superior in detecting the true lithological classes when compared to more frequently applied unsupervised classification approaches or literature classification diagrams. We demonstrate that automated mapping of lithostratigraphic units using advanced SBT classification techniques can provide a large gain in efficiency, compared to more time-consuming manual approaches and yields at least equally accurate results. [ABSTRACT FROM AUTHOR]

Comprehensive learning particle swarm optimization (CLPSO) is a powerful state-of-the-art single-objective metaheuristic. Extending from CLPSO, this paper proposes multiswarm CLPSO (MSCLPSO) for multiobjective optimization. MSCLPSO involves multiple swarms, with each swarm associated with a separate original objective. Each particle’s personal best position is determined just according to the corresponding single objective. Elitists are stored externally. MSCLPSO differs from existing multiobjective particle swarm optimizers in three aspects. First, each swarm focuses on optimizing the associated objective using CLPSO, without learning from the elitists or any other swarm. Second, mutation is applied to the elitists and the mutation strategy appropriately exploits the personal best positions and elitists. Third, a modified differential evolution (DE) strategy is applied to some extreme and least crowded elitists. The DE strategy updates an elitist based on the differences of the elitists. The personal best positions carry useful information about the Pareto set, and the mutation and DE strategies help MSCLPSO discover the true Pareto front. Experiments conducted on various benchmark problems demonstrate that MSCLPSO can find nondominated solutions distributed reasonably over the true Pareto front in a single run. [ABSTRACT FROM AUTHOR]

In this paper we explore a relational understanding of landscape qualities. We asked three independent groups of human observers to assess the expressive qualities of a range of landscapes in the UK and in Spain, either by means of personal visits or from a projected digital image. We employed a Free Choice Profiling (FCP) methodology, in which observers generated their own descriptive terminologies and then used these to quantify perceived landscape qualities on visual analogue scales. Data were analysed using Generalised Procrustes Analysis, a multivariate statistical technique that does not rely on fixed variables to identify underlying dimensions of assessment. The three observer groups each showed significant agreement, and generated two main consensus dimensions that suggested landscape ‘health’ and ‘development in time’ as common perceived themes of landscape expressivity. We critically discuss these outcomes in context of the landscape assessment literature, and suggest ways forward for further development and research. [ABSTRACT FROM AUTHOR]

Media, and particularly TV media, have a great impact on the general public. In recent years, spatial patterns of information and the relevance of intangible geographies have become increasingly important. Gatekeeping plays a critical role in the selection of information that is transformed into media. Therefore, gatekeeping, through national media, also co-forms the generation of mental maps. In this paper, correspondence analysis (a statistical method) combined with cloud lines (a new visual analytics technique) is used to analyze how individual major regional events in one of the post-communist countries, the Czech Republic, penetrate into the media on a national scale. Although national news should minimize distortions about regions, this assumption has not been verified by our research. Impressions presented by the media of selected regions that were markedly influenced by one or several events in those regions demonstrate that gatekeepers, especially news reporters, functioned as a filter by selecting only a few specific, and in many cases, unusual events for dissemination. [ABSTRACT FROM AUTHOR]

Refraction techniques make it possible to determine the most appropriate sphero-cylindrical lens prescription to achieve the best possible visual quality. Among these techniques, subjective refraction (i.e., patient’s response-guided refraction) is the most commonly used approach. In this context, this paper’s main goal is to present a simulation software that implements in a virtual manner various subjective-refraction techniques—including Jackson’s Cross-Cylinder test (JCC)—relying all on the observation of computer-generated retinal images. This software has also been used to evaluate visual quality when the JCC test is performed in multifocal-contact-lens wearers. The results reveal this software’s usefulness to simulate the retinal image quality that a particular visual compensation provides. Moreover, it can help to gain a deeper insight and to improve existing refraction techniques and it can be used for simulated training. [ABSTRACT FROM AUTHOR]

We examine individual-level trading data from several markets in the PredictIt exchange to determine what strategies correlate with financial success. PredictIt provides many markets with futures contracts linked to political issues, ranging from ongoing policy outcomes to political elections. High fees along with restrictions blocking automatic trading and constraining a one-to-one match between people and accounts, combine to severely limit the upside to investment returns over the fixed costs: this ensures that traders are all retail investors. We have the individual-level data from two markets: Democratic and Republican Iowa Caucuses in 2016. This data includes all orders and trades from every trader across the markets. We are able to fully reconstruct market activity and study trader behavior both within and between markets. We show that understanding how markets and trades works is more important to financial success than proxies for (1) confidence or funding (2) information or objectivity in trading. The work should be a call-to-action in favor of simplifying markets and trading for any exchange with retail investors, and for more research into effects of differential trading efficiency in all financial markets. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the clinical significance of color visual acuity (CVA) in preperimetric glaucoma (PPG) and open-angle glaucoma (OAG). Methods: A total of 123 eyes of 73 subjects (22 normal eyes, 14 PPG eyes, and 87 OAG eyes; mean age: 44.9 ± 10.1 years, age range: 21–64 years) were enrolled. CVA was tested for red, green-yellow, blue-green and blue-purple with a newly developed test. Results: There was no statistical difference in clinical background factors, including age, sex, intraocular pressure, or spherical equivalent between the three groups. Red VA and blue-green VA were significantly worse in the OAG eyes than in the normal eyes (P = 0.008 and P = 0.015, respectively), although green-yellow VA and blue-purple VA were not significantly worse. Furthermore, red VA and blue-green VA were significantly correlated with MD in a group of eyes with either PPG or OAG (r = -0.23, P = 0.023; r = -0.25, P = 0.012, respectively), but green-yellow VA and blue-purple VA were not. Conclusion: Red VA and blue-green VA were detectably worse in eyes with OAG, in close association with the degree of functional loss. This suggests that measuring CVA with the new color test described here may be a promising supplement to existing methods of detecting glaucoma and evaluating its severity. [ABSTRACT FROM AUTHOR]

Background: The implementation of Computerised Physician Order Entry (CPOE) and Clinical Decision Support (CDS) has been found to have some unintended consequences. The aim of this study is to explore pharmacists and physicians perceptions of their interprofessional communication in the context of the technology and whether electronic messaging and CDS has an impact on this. Method: This qualitative study was conducted in two acute hospitals: the University Hospitals Birmingham NHS Foundation Trust (UHBFT) and Guy’s and St Thomas’ NHS Foundation Trust (GSTH). UHBFT use an established locally developed CPOE system that can facilitate pharmacist-physician communication with the ability to assign a message directly to an electronic prescription. In contrast, GSTH use a more recently implemented commercial system where such communication is not possible. Focus groups were conducted with pharmacists and physicians of varying grades at both hospitals. Focus group data were transcribed and analysed thematically using deductive and inductive approaches, facilitated by NVivo 10. Results: Three prominent themes emerged during the study: increased communication load; impaired decision-making; and improved workflow. CPOE and CDS were found to increase the communication load for the pharmacist owing to a reduced ability to amend electronic prescriptions, new types of prescribing errors, and the provision of technical advice relating to the use of the system. Decision-making was found to be affected, owing to the difficulties faced by pharmacists and physicians when trying to determine the context of prescribing decisions and knowledge of the patient. The capability to communicate electronically facilitated a non-interruptive workflow, which was found to be beneficial for staff time, coordination of work and for limiting distractions. Conclusion: The increased communication load for the pharmacist, and consequent workload for the physician, has the potential to impact on the quality and coordination of care in the hospital setting. The ability to communicate electronically has some benefits, but functions need to be designed to facilitate collaborative working, and for this to be optimised through interprofessional training. [ABSTRACT FROM AUTHOR]

Objective: To explore, through face to face interviews with a selection of General Dental Practitioners (GDPs), their views and experiences of having used the International Caries Detection and Assessment System (ICDAS) within primary care research studies for recording caries. Methods: This qualitative study involved one on one interviews with eight GDP’s who had previously used ICDAS on patients in their dental practices as part of a research study. The participants were selected from among those who had taken part in two clinical studies in the UK using convenient, but purposive sampling. The interviews were tape-recorded and transcribed; the data analysis was conducted by thematic analysis. Results: GDP’s indicated their beliefs that ICDAS had an important role in caries prevention but reported four main barriers while using the full (6 caries stages) ICDAS coding system in their practices: lack of simplicity of coding, financial implications and time consumption (in both training and use of ICDAS) and inadequate undergraduate training. An overarching theme identified from the GDPS was the willingness to offer potential solutions to their barriers which might improve the utilisation of the system in primary care. Conclusion: The GDPs experienced common obstacles in using ICDAS in the primary care setting, many of which have relatively straight-forward solutions which they put forward themselves such as: incentivisation, undergraduate-level training in ICDAS for both dentists and nurses and computerized data entry. Further qualitative and quantitative research is needed on how to facilitate the utilisation of the system in dental practice. It is also recommended to explore the influences of wider agencies on influencing primary dental care professionals’ caries management, including appropriate recording of diagnosis and risk assessment. [ABSTRACT FROM AUTHOR]

Background: Preterm infants are a group at high risk of having experienced placental insufficiency. It is unclear which growth charts perform best in identifying infants at increased risk of stillbirth and other adverse perinatal outcomes. We compared 2 birthweight charts (population centiles and INTERGROWTH-21st birthweight centiles) and 3 fetal growth charts (INTERGROWTH-21st fetal growth charts, World Health Organization fetal growth charts, and Gestation Related Optimal Weight [GROW] customised growth charts) to identify which chart performed best in identifying infants at increased risk of adverse perinatal outcome in a preterm population.Methods and Findings: We conducted a retrospective cohort study of all preterm infants born at 24.0 to 36.9 weeks gestation in Victoria, Australia, from 2005 to 2015 (28,968 records available for analysis). All above growth charts were applied to the population. Proportions classified as <5th centile and <10th centile by each chart were compared, as were proportions of stillborn infants considered small for gestational age (SGA, <10th centile) by each chart. We then compared the relative performance of non-overlapping SGA cohorts by each chart to our low-risk reference population (infants born appropriate size for gestational age [>10th and <90th centile] by all intrauterine charts [AGAall]) for the following perinatal outcomes: stillbirth, perinatal mortality (stillbirth or neonatal death), Apgar <4 or <7 at 5 minutes, neonatal intensive care unit admissions, suspicion of poor fetal growth leading to expedited delivery, and cesarean section. All intrauterine charts classified a greater proportion of infants as <5th or <10th centile than birthweight charts. The magnitude of the difference between birthweight and fetal charts was greater at more preterm gestations. Of the fetal charts, GROW customised charts classified the greatest number of infants as SGA (22.3%) and the greatest number of stillborn infants as SGA (57%). INTERGROWTH classified almost no additional infants as SGA that were not already considered SGA on GROW or WHO charts; however, those infants classified as SGA by INTERGROWTH had the greatest risk of both stillbirth and total perinatal mortality. GROW customised charts classified a larger proportion of infants as SGA, and these infants were still at increased risk of mortality and adverse perinatal outcomes compared to the AGAall population. Consistent with similar studies in this field, our study was limited in comparing growth charts by the degree of overlap, with many infants classified as SGA by multiple charts. We attempted to overcome this by examining and comparing sub-populations classified as SGA by only 1 growth chart.Conclusions: In this study, fetal charts classified greater proportions of preterm and stillborn infants as SGA, which more accurately reflected true fetal growth restriction. Of the intrauterine charts, INTERGROWTH classified the smallest number of preterm infants as SGA, although it identified a particularly high-risk cohort, and GROW customised charts classified the greatest number at increased risk of perinatal mortality. [ABSTRACT FROM AUTHOR]

Background: Unplanned readmission of a hospitalized patient is an indicator of patients’ exposure to risk and an avoidable waste of medical resources. In addition to hospital readmission, intensive care unit (ICU) readmission brings further financial risk, along with morbidity and mortality risks. Identification of high-risk patients who are likely to be readmitted can provide significant benefits for both patients and medical providers. The emergence of machine learning solutions to detect hidden patterns in complex, multi-dimensional datasets provides unparalleled opportunities for developing an efficient discharge decision-making support system for physicians and ICU specialists. Methods and findings: We used supervised machine learning approaches for ICU readmission prediction. We used machine learning methods on comprehensive, longitudinal clinical data from the MIMIC-III to predict the ICU readmission of patients within 30 days of their discharge. We incorporate multiple types of features including chart events, demographic, and ICD-9 embeddings. We have utilized recent machine learning techniques such as Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM), by this we have been able to incorporate the multivariate features of EHRs and capture sudden fluctuations in chart event features (e.g. glucose and heart rate). We show that our LSTM-based solution can better capture high volatility and unstable status in ICU patients, an important factor in ICU readmission. Our machine learning models identify ICU readmissions at a higher sensitivity rate of 0.742 (95% CI, 0.718–0.766) and an improved Area Under the Curve of 0.791 (95% CI, 0.782–0.800) compared with traditional methods. We perform in-depth deep learning performance analysis, as well as the analysis of each feature contribution to the predictive model. Conclusion: Our manuscript highlights the ability of machine learning models to improve our ICU decision-making accuracy and is a real-world example of precision medicine in hospitals. These data-driven solutions hold the potential for substantial clinical impact by augmenting clinical decision-making for physicians and ICU specialists. We anticipate that machine learning models will improve patient counseling, hospital administration, allocation of healthcare resources and ultimately individualized clinical care. [ABSTRACT FROM AUTHOR]

Background: Administrative healthcare databases are useful and inexpensive tools that can provide a comprehensive assessment of the burden of diseases in terms of major outcomes, such as mortality, hospital readmissions, and use of healthcare resources. However, a crucial issue is the reliability of information gathered. The aim of this study was to validate ICD-9 codes for several major cardiovascular conditions, i.e., acute myocardial infarction (AMI), atrial fibrillation/flutter (AF), and heart failure (HF), in order to use them for epidemiological, outcome, and health services research. Methods: Data from the centralised administrative database of the Umbria Region (890,000 residents, located in Central Italy) were considered. Patients with a first hospital discharge for AMI, AF/flutter, and HF, between 2012 and 2014, were identified using ICD-9-CM codes in primary position. A sample of cases and non-cases was randomly selected, and the corresponding medical charts reviewed by specifically trained investigators. For each disease, case ascertainment was based on all clinical, laboratory, and instrumental examinations available in medical charts. Sensitivity, specificity, and predictive values with 95% confidence intervals (CIs), were calculated. Results: We reviewed 458 medical charts, 128 for AMI, 127 for AF/flutter, 127 for HF, and 76 of non-cases for each condition. Diagnostic accuracy measures of the original discharge diagnosis were as follows. AMI: sensitivity 98% (95% CI, 94–100%), specificity 91% (95% CI, 83–97%), positive predictive value (PPV) 95% (95% CI, 89–98%), negative predictive value (NPV) 97% (95% CI, 91–100%). AF/flutter: sensitivity 95% (95% CI, 90–98%), specificity 95% (95% CI, 87–99%), PPV 97% (95% CI, 92–99%), NPV 92% (95% CI, 84–97%). HF: sensitivity 96% (95% CI, 91–99%), specificity 90% (95% CI, 81–96%), PPV 94% (95% CI, 88–97%), NPV 93% (95% CI, 85–98%). Conclusion: The case ascertainment for AMI, AF and flutter, and HF, showed a high level of accuracy (≥ 90%). The healthcare administrative database of the Umbria Region can be confidently used for epidemiological, outcome, and health services research. [ABSTRACT FROM AUTHOR]

Purpose: First, to evaluate inter-rater reliability when human raters estimate the reading performance of visually impaired individuals using the MNREAD acuity chart. Second, to evaluate the agreement between computer-based scoring algorithms and compare them with human rating. Methods: Reading performance was measured for 101 individuals with low vision, using the Portuguese version of the MNREAD test. Seven raters estimated the maximum reading speed (MRS) and critical print size (CPS) of each individual MNREAD curve. MRS and CPS were also calculated automatically for each curve using two different algorithms: the original standard deviation method (SDev) and a non-linear mixed effects (NLME) modeling. Intra-class correlation coefficients (ICC) were used to estimate absolute agreement between raters and/or algorithms. Results: Absolute agreement between raters was ‘excellent’ for MRS (ICC = 0.97; 95%CI [0.96, 0.98]) and ‘moderate’ to ‘good’ for CPS (ICC = 0.77; 95%CI [0.69, 0.83]). For CPS, inter-rater reliability was poorer among less experienced raters (ICC = 0.70; 95%CI [0.57, 0.80]) when compared to experienced ones (ICC = 0.82; 95%CI [0.76, 0.88]). Absolute agreement between the two algorithms was ‘excellent’ for MRS (ICC = 0.96; 95%CI [0.91, 0.98]). For CPS, the best possible agreement was found for CPS defined as the print size sustaining 80% of MRS (ICC = 0.77; 95%CI [0.68, 0.84]). Absolute agreement between raters and automated methods was ‘excellent’ for MRS (ICC = 0.96; 95% CI [0.88, 0.98] for SDev; ICC = 0.97; 95% CI [0.95, 0.98] for NLME). For CPS, absolute agreement between raters and SDev ranged from ‘poor’ to ‘good’ (ICC = 0.66; 95% CI [0.3, 0.80]), while agreement between raters and NLME was ‘good’ (ICC = 0.83; 95% CI [0.76, 0.88]). Conclusion: For MRS, inter-rater reliability is excellent, even considering the possibility of noisy and/or incomplete data collected in low-vision individuals. For CPS, inter-rater reliability is lower. This may be problematic, for instance in the context of multisite investigations or follow-up examinations. The NLME method showed better agreement with the raters than the SDev method for both reading parameters. Setting up consensual guidelines to deal with ambiguous curves may help improve reliability. While the exact definition of CPS should be chosen on a case-by-case basis depending on the clinician or researcher’s motivations, evidence suggests that estimating CPS as the smallest print size sustaining about 80% of MRS would increase inter-rater reliability. [ABSTRACT FROM AUTHOR]

Background: End-of-life (EOL) decisions are a serious ethical dilemma and are frequently carried out in intensive care units (ICUs). The aim of this systematic review was to investigated the different approaches used in ICUs and reported in randomized controlled trials (RCTs) to address EOL decisions and compare the impact of these different strategies regarding potential bias and mortality estimates. Methods: We identified relevant RCTs published in the past 15 years via PubMed, EMBASE, and CINAHL. In addition, we searched The Cochrane Library and checked registries, including ClinicalTrials.gov to assess concordance between declared and published outcomes. Among the journals we screened were the 3 ICU specialty journals and the four general medicine journals with the highest impact factor. Only RCTs were selected in which in-ICU mortality was the primary or secondary outcome. The primary outcome was information regarding EOL decisions, and the secondary outcome was how EOL decisions were treated in the study analysis. Results: A total of 178 relevant trials were identified. The details regarding the methodological aspects resulting from EOL decisions were reported in only 62 articles (35%). The manner in which EOL decisions were considered in the study analysis was very heterogeneous, often leading to a high risk of bias. Conclusion: There is a heterogeneity regarding the management of data on EOL decisions in randomized control trials with mortality endpoints. Recommendations or rules are required regarding the inclusion of patients with potential EOL decisions in RCT analyses and how to manage such decisions from a methodological point of view. Trial registration: PROSPERO website (). [ABSTRACT FROM AUTHOR]

Background: Administrative data are used to examine variation in thirty-day mortality across health services in several jurisdictions. Hospital performance measurement may be error-prone as information about disease severity is not typically available in routinely collected data to incorporate into case-mix adjusted analyses. Using ischaemic stroke as a case study, we tested the extent to which accounting for disease severity impacts on hospital performance assessment. Methods: We linked all recorded ischaemic stroke admissions between July, 2011 and June, 2014 to death registrations and a measure of stroke severity obtained at first point of patient contact with health services, across New South Wales, Australia’s largest health service jurisdiction. Thirty-day hospital standardised mortality ratios were adjusted for either comorbidities, as is typically done, or for both comorbidities and stroke severity. The impact of stroke severity adjustment on mortality ratios was determined using 95% and 99% control limits applied to funnel plots and by calculating the change in rank order of hospital risk adjusted mortality rates. Results: The performance of the stroke severity adjusted model was superior to incorporating comorbidity burden alone (c-statistic = 0.82 versus 0.75; N = 17,700 patients, 176 hospitals). Concordance in outlier classification was 89% and 97% when applying 95% or 99% control limits to funnel plots, respectively. The sensitivity rates of outlier detection using comorbidity adjustment compared with gold-standard severity and comorbidity adjustment was 74% and 83% with 95% and 99% control limits, respectively. Corresponding positive predictive values were 74% and 91%. Hospital rank order of risk adjusted mortality rates shifted between 0 to 22 places with severity adjustment (Median = 4.0, Inter-quartile Range = 2–7). Conclusions: Rankings of mortality rates varied widely depending on whether stroke severity was taken into account. Funnel plots yielded largely concordant results irrespective of severity adjustment and may be sufficiently accurate as a screening tool for assessing hospital performance. [ABSTRACT FROM AUTHOR]

In two studies we provide a novel investigation into the effects of monetary switching costs on choice-inertia (i.e., selection of the same option on consecutive choices). Study 1 employed a static decisions-from-feedback task and found that the introduction of, as well as larger, monetary switching costs led to increases in choice-inertia. While experience and decreases in the similarity of options average payouts (expected value: EV) increased choice-inertia for the option with a higher EV (the EV maximizing option), switching costs increased choice-inertia for the inferior option (the lower EV option): The proportion of total participants showing choice-inertia for the EV maximizing option also increased with switching costs. Study 2 employed a dynamic decisions-from-feedback task where halfway through the task the EV maximizing option became the inferior option. The effect of switching costs increasing choice-inertia for both the EV maximizing and the inferior option was replicated with little impact of the change in options values being detected. In sum, decision makers appear to be sensitive to switching costs, and this sensitivity can bias them towards inferior or superior options, revealing the good and the bad of choice-inertia. [ABSTRACT FROM AUTHOR]

Background: The Cognitive Orientation to daily Occupational Performance (CO-OP) approach is a complex rehabilitation intervention in which clients are taught to use problem-solving cognitive strategies to acquire personally-meaningful functional skills, and health care providers are required to shift control regarding treatment goals and intervention strategies to their clients. A multi-faceted, supported, knowledge translation (KT) initiative was targeted at the implementation of CO-OP in inpatient stroke rehabilitation teams at five freestanding rehabilitation hospitals. The study objective was to estimate changes in rehabilitation clinicians’ knowledge, self-efficacy, and practice related to implementing CO-OP. Methods: A single arm pre-post and 6-month follow up study was conducted. CO-OP KT consisted of a 2-day workshop, 4 months of implementation support, a consolidation session, and infrastructure support. In addition, a sustainability plan was implemented. Consistent with CO-OP principles, teams were given control over specific implementation goals and strategies. Multiple choice questions (MCQ) were used to assess knowledge. A self-efficacy questionnaire with 3 subscales (Promoting Cognitive Strategy Use, PCSU; Client-Focused Therapy, CFT; Top-Down Assessment and Treatment, TDAT) was developed for the study. Medical record audits were used to investigate practice change. Data analysis for knowledge and self-efficacy utilized mixed effects models. Medical record audits were analyzed with frequency counts and chi-squares. Results: Sixty-five health care providers consisting mainly of occupational and physical therapists entered the study. Mixed effects models revealed intervention effects for MCQs, CFT, and PCSU at post intervention and follow-up, but no effect on TDAT. No charts showed any evidence of CO-OP use at baseline, compared to 8/40 (20%) post intervention. Post intervention there was a trend towards reduction in impairment goals and significantly more component goals were set (z = 2.7, p = .007). [ABSTRACT FROM AUTHOR]

Forecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. We propose a model, called the feature fusion long short-term memory-convolutional neural network (LSTM-CNN) model, that combines features learned from different representations of the same data, namely, stock time series and stock chart images, to predict stock prices. The proposed model is composed of LSTM and a CNN, which are utilized for extracting temporal features and image features. We measure the performance of the proposed model relative to those of single models (CNN and LSTM) using SPDR S&P 500 ETF data. Our feature fusion LSTM-CNN model outperforms the single models in predicting stock prices. In addition, we discover that a candlestick chart is the most appropriate stock chart image to use to forecast stock prices. Thus, this study shows that prediction error can be efficiently reduced by using a combination of temporal and image features from the same data rather than using these features separately. [ABSTRACT FROM AUTHOR]

How well do we remember popular music? To investigate how hit songs are recognized over time, we randomly selected number-one Billboard singles from the last 76 years and presented them to a large sample of mostly millennial participants. In response to hearing each song, participants were prompted to indicate whether they recognized it. Plotting the recognition proportion for each song as a function of the year during which it reached peak popularity resulted in three distinct phases in collective memory. The first phase is characterized by a steep linear drop-off in recognition for the music from this millennium; the second phase consists of a stable plateau during the 1960s to the 1990s; and the third phase, a further but more gradual drop-off during the 1940s and 1950s. More than half of recognition variability can be accounted for by self-selected exposure to each song as measured by its play count on Spotify. We conclude that collective memory for popular music is different from that of other historical phenomena. [ABSTRACT FROM AUTHOR]

Purpose: Clinical assessment of a new optical element for presbyopia correction–the Light Sword Lens. Methods: Healthy dominant eyes of 34 presbyopes were examined for visual performance in 3 trials: reference (with lens for distance correction); stenopeic (distance correction with a pinhole ϕ = 1.25 mm) and Light Sword Lens (distance correction with a Light Sword Lens). In each trial, visual acuity was assessed in 7 tasks for defocus from 0.2D to 3.0D while contrast sensitivity in 2 tasks for defocus 0.3D and 2.5D. The Early Treatment Diabetic Retinopathy Study protocol and Pelli-Robson method were applied. Within visual acuity and contrast sensitivity results degree of homogeneity through defocus was determined. Reference and stenopeic trials were compared to Light Sword Lens results. Friedman analysis of variance, Nemenyi post-hoc, Wilcoxon tests were used, p-value < 0.05 was considered significant. Results: In Light Sword Lens trial visual acuity was stable in tested defocus range [20/25–20/32], Stenopeic trial exhibited a limited range of degradation [20/25–20/40]. Light Sword Lens and reference trials contrast sensitivity was high [1.9–2.0 logCS] for both defocus cases, but low in stenopeic condition [1.5–1.7 logCS]. Between-trials comparisons of visual acuity results showed significant differences only for Light Sword Lens versus reference trials and in contrast sensitivity only for Light Sword Lens versus stenopeic trials. Conclusions: Visual acuity achieved with Light Sword Lens correction in presbyopic eye is comparable to stenopeic but exhibits none significant loss in contrast sensitivity. Such correction method seems to be very promising for novel contact lenses and intraocular lenses design. [ABSTRACT FROM AUTHOR]

Background: Expressing anthropometric parameters (height, weight, BMI) as z-score is a key principle in the clinical assessment of children and adolescents. The Centre for Disease Control and Prevention (CDC) growth charts and the CDC-LMS method for z-score calculation are widely used to assess growth and nutritional status, though they can be imprecise in some percentiles. Objective: To improve the accuracy of z-score calculation by revising the statistical method using the original data used to develop current z-score calculators. Design: A Gaussian Process Regressions (GPR) was designed and internally validated. Z-scores for weight-for-age (WFA), height-for-age (HFA) and BMI-for-age (BMIFA) were compared with WHO and CDC-LMS methods in 1) standard z-score cut-off points, 2) simulated population of 3000 children and 3) real observations 212 children aged 2 to 18 yo. Results: GPR yielded more accurate calculation of z-scores for standard cut-off points (p<<0.001) with respect to CDC-LMS and WHO approaches. WFA, HFA and BMIFA z-score calculations based on the 3 different methods using simulated and real patients, showed a large variation irrespective of gender and age. Z-scores around 0 +/- 1 showed larger variation than the values above and below +/- 2. Conclusion: The revised z-score calculation method was more accurate than CDC-LMS and WHO methods for standard cut-off points. On simulated and real data, GPR based calculation provides more accurate z-score determinations, and thus, a better classification of patients below and above cut-off points. Statisticians and clinicians should consider the potential benefits of updating their calculation method for an accurate z-score determination. [ABSTRACT FROM AUTHOR]

Background and aims: Eustachian tube dysfunction (ETD) is a commonly diagnosed disorder of Eustachian tube opening and closure, which may be associated with severe symptoms and middle ear disease. Currently the diagnosis of obstructive and patulous forms of ETD is primarily based on non-specific symptoms or examination findings, rather than measurement of the underlying function of the Eustachian tube. This has proved problematic when selecting patients for treatment, and when designing trial inclusion criteria and outcomes. This study aims to determine the correlation and diagnostic value of various tests of ET opening and patient reported outcome measures (PROMs), in order to generate a recommended diagnostic pathway for ETD. Methods: Index tests included two PROMs and 14 tests of ET opening (nine for obstructive, five for patulous ETD). In the absence of an accepted reference standard two methods were adopted to establish index test accuracy: expert panel diagnosis and latent class analysis. Index test results were assessed with Pearson correlation and principle component analysis, and test accuracy was determined. Logistic regression models assessed the predictive value of grouped test results. Results: The expert panel diagnosis and PROMs results correlated with each other, but not with ET function measured by tests of ET opening. All index tests were found to be feasible in clinic, and acceptable to patients. PROMs had very poor specificity, and no diagnostic value. Combining the results of tests of ET function appeared beneficial. The latent class model suggested tympanometry, sonotubometry and tubomanometry have the best diagnostic performance for obstructive ETD, and these are included in a proposed diagnostic pathway. Conclusions: ETD should be diagnosed on the basis of clinical assessment and tests of ET opening, as PROMs have no diagnostic value. Currently diagnostic uncertainty exists for some patients who appear to have intermittent ETD clinically, but have negative index test results. [ABSTRACT FROM AUTHOR]

Objective: We assess change in bleeding, cramping, and IUD satisfaction among new copper (Cu) IUD users during the first six months of use, and evaluate the impact of bleeding and cramping on method satisfaction. Methods: We recruited 77 women ages 18–45 for this prospective longitudinal observational cohort study. Eligible women reported regular menses, had no exposure to hormonal contraception in the last three months, and desired a Cu IUD for contraception. We collected data prospectively for 180 days following IUD insertion. Monthly, participants reported bleeding scores using the validated pictorial blood loss assessment chart (PBAC), IUD satisfaction using a five-point Likert scale, and cramping using a six-level ordinal scale. We used multiple imputation to address nonrandom attrition. Structural equation models for count and ordered outcomes were used to model bleeding, cramping, and IUD satisfaction growth curves over the six monthly repeated assessments. Results: Bleeding significantly decreased (approximately 23%) over the course of the study from an estimated PBAC = 195 at one month post-insertion to PBAC = 151 at six months (t = -2.38, p<0.05). Additionally, IUD satisfaction improved over time (t = 2.65, p<0.01), increasing from between “Neutral” and “Satisfied” to “Satisfied” over the six month study. Cramping decreased notably over the six month study from between biweekly and weekly, to once or twice a month (t = -4.38, p<0.001). Finally, bleeding, but not cramping, was associated with IUD satisfaction across the study (t = -2.31, p<0.05) and at study end (t = -2.81, p<0.01). Conclusions: New Cu IUD users reported decreasing bleeding and cramping, and increasing IUD satisfaction, over the first six months. Method satisfaction was negatively associated with bleeding. [ABSTRACT FROM AUTHOR]

Background: Preventable adverse drug reactions (PADRs) in inpatients are associated with harm, including increased length of stay and potential loss of life, and result in elevated costs of care. We conducted an overview of reviews (i.e., a systematic review of systematic reviews) to determine the incidence of PADRs experienced by inpatients. Secondary review objectives were related to assessment of the effects of patient age, setting, and clinical specialty on PADR incidence. Methods: The protocol was registered in PROSPERO (CRD42016043220). We performed a search of Medline, Embase, and the Cochrane Library, limiting languages of publication to English and French. We included published systematic reviews that reported quantitative data on the incidence of PADRs in patients receiving acute or ambulatory care in a hospital setting. The full texts of all primary studies for which PADR data were reported in the included reviews were obtained and data relevant to review objectives were extracted. Quality of the included reviews was assessed using the AMSTAR-2 tool. Both narrative summaries of findings and meta-analyses of primary study data were undertaken. Results: Thirteen systematic reviews encompassing 37 unique primary studies were included. Across primary studies, the PADR incidence was highly varied, ranging from 0.006 to 13.3 PADRs per 100 patients, with a pooled incidence estimate of 0.59 PADRs per 100 patients. Substantial heterogeneity was present across both reviews and primary studies with respect to review/study objectives, patient age, hospital setting, medical discipline, definitions and assessment tools used, event detection methods, endpoints of interest, and units of measure. Thirteen primary studies used prospective event detection methods and had a pooled PADR incidence of 3.13 (2.87–3.38) PADRs per 100 patients; however, extreme statistical heterogeneity (I2 = 97%) indicated this finding should be considered with caution. Subgroup meta-analyses demonstrated that PADR incidence varied significantly with event detection method (prospective > retrospective > voluntary reporting methods), hospital setting (ICU > wards), and medical discipline (medical > surgical). High statistical heterogeneity (I2 > 80%) was present across all analyses, indicating results should be interpreted with caution. Effects of patient age could not be assessed due to poor reporting of age groups used in primary studies. Discussion: The method of event detection appeared to significantly influence PADR incidence, with prospective methods having the highest reported PADR rate. This finding is in agreement with the background literature. High methodological and statistical heterogeneity across primary studies evaluating adverse drug events reduces the validity of the overall PADR incidence derived from the meta-analyses of the pooled data. Data pooled from studies using only prospective methods of event detection should provide an overall estimate closest to the true PADR incidence; however, our estimate should be considered with caution due to the statistical heterogeneity found in this group of studies. Future studies should employ prospective methods of detection. This review demonstrates that the true overall incidence of PADRs is likely much greater than the overall pooled incidence estimate of 0.59 PADRs per 100 patients obtained when event detection method was not taken into consideration. [ABSTRACT FROM AUTHOR]

Background: Hydrocephalus is a debilitating disorder, affecting all age groups. Evaluation of its global epidemiology is required for healthcare planning and resource allocation. Objectives: To define age-specific global prevalence and incidence of hydrocephalus. Methods: Population-based studies reporting prevalence of hydrocephalus were identified (MEDLINE, EMBASE, Cochrane, and Google Scholar (1985–2017)). Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. Two authors reviewed abstracts, full text articles and abstracted data. Metanalysis and meta-regressions were used to assess associations between key variables. Heterogeneity and publication bias were assessed. Main outcome of interest was hydrocephalus prevalence among pediatric (≤ 18 years), adults (19–64 years), and elderly (≥ 65) patients. Annual hydrocephalus incidence stratified by country income level and folate fortification requirements were obtained (2003–2014) from the International Clearinghouse for Birth Defects Surveillance and Research (ICBDSR). Results: Of 2,460 abstracts, 52 met review eligibility criteria (aggregate population 171,558,651). Mean hydrocephalus prevalence was 85/100,000 [95% CI 62, 116]. The prevalence was 88/100,000 [95% CI 72, 107] in pediatrics; 11/100,000 [95% CI 5, 25] in adults; and 175/100,000 [95% CI 67, 458] in the elderly. The ICBDSR-based incidence of hydrocephalus diagnosed at birth remained stable over 11 years: 81/100,000 [95% CI 69, 96]. A significantly lower incidence was identified in high-income countries. Conclusion: This systematic review established age-specific global hydrocephalus prevalence. While high-income countries had a lower hydrocephalus incidence according to the ICBDSR registry, folate fortification status was not associated with incidence. Our findings may inform future healthcare resource allocation and study. [ABSTRACT FROM AUTHOR]

Comparing the functional performance of biological systems often requires comparing multiple mechanical properties. Such analyses, however, are commonly presented using orthogonal plots that compare N ≤ 3 properties. Here, we develop a multidimensional visualization strategy using permutated radar charts (radial, multi-axis plots) to compare the relative performance distributions of mechanical systems on a single graphic across N ≥ 3 properties. Leveraging the fact that radar charts plot data in the form of closed polygonal profiles, we use shape descriptors for quantitative comparisons. We identify mechanical property-function correlations distinctive to rigid, flexible, and damage-tolerant biological materials in the form of structural ties, beams, shells, and foams. We also show that the microstructures of dentin, bone, tendon, skin, and cartilage dictate their tensile performance, exhibiting a trade-off between stiffness and extensibility. Lastly, we compare the feeding versus singing performance of Darwin’s finches to demonstrate the potential of radar charts for multidimensional comparisons beyond mechanics of materials. [ABSTRACT FROM AUTHOR]

We propose a nonparametric risk-adjusted cumulative sum chart to monitor surgical outcomes for patients with different risks of post-operative mortality due to risk factors that exist before the surgery. Using varying-coefficient logistic regression models, we accomplish the risk adjustment. Unknown coefficient functions are estimated by global polynomial spline approximation based on the maximum likelihood principle. We suggest a bisection minimization approach and a bootstrap method to determine the chart testing limit value. Compared with the previous (parametric) risk-adjusted cumulative sum chart, a major advantage of our method is that the morality rate can be modeled more flexibly by related covariates, which significantly enhances the monitoring efficiency. Simulations demonstrate nice performance of our proposed procedure. An application to a UK cardiac surgery dataset illustrates the use of our methodology. [ABSTRACT FROM AUTHOR]

Introduction: A required step for presenting results of clinical studies is the declaration of participants demographic and baseline characteristics as claimed by the FDAAA 801. The common workflow to accomplish this task is to export the clinical data from the used electronic data capture system and import it into statistical software like SAS software or IBM SPSS. This software requires trained users, who have to implement the analysis individually for each item. These expenditures may become an obstacle for small studies. Objective of this work is to design, implement and evaluate an open source application, called ODM Data Analysis, for the semi-automatic analysis of clinical study data. Methods: The system requires clinical data in the CDISC Operational Data Model format. After uploading the file, its syntax and data type conformity of the collected data is validated. The completeness of the study data is determined and basic statistics, including illustrative charts for each item, are generated. Datasets from four clinical studies have been used to evaluate the application’s performance and functionality. Results: The system is implemented as an open source web application (available at ) and also provided as Docker image which enables an easy distribution and installation on local systems. Study data is only stored in the application as long as the calculations are performed which is compliant with data protection endeavors. Analysis times are below half an hour, even for larger studies with over 6000 subjects. Discussion: Medical experts have ensured the usefulness of this application to grant an overview of their collected study data for monitoring purposes and to generate descriptive statistics without further user interaction. The semi-automatic analysis has its limitations and cannot replace the complex analysis of statisticians, but it can be used as a starting point for their examination and reporting. [ABSTRACT FROM AUTHOR]

Purpose: To highlight the potential shortcomings associated with the current use Rasch analysis for validation of ophthalmic questionnaires, and to present an alternative application of Rasch analysis to derive insights specific to the cohort of patients under investigation. Methods: An alternative application of Rasch analysis was used to investigate the quality of vision (QoV) for a cohort of 481 patients. Patients received multifocal intraocular lenses and completed a QoV questionnaire one and twelve months post-operatively. The rating scale variant of the polytomous Rasch model was utilized. The parameters of the model were estimated using the joint maximum likelihood estimation. Analysis was performed on data at both post-operative assessments, and the outcomes were compared. Results: The distribution of the location of symptoms altered between assessments with the most annoyed patients completely differing. One month post-operatively, the most prevalent symptom was starbursts compared to glare at twelve months. The visual discomfort from the most annoyed patients is substantially higher at twelve months. The current most advocated approach for validating questionnaires using Rasch analysis found that the questionnaire was “Rasch-valid” one month post-operatively and “Rasch-invalid” twelve months post-operatively. Conclusion: The proposed alternative application of Rasch analysis to questionnaires can be used as an effective decision support tool at population and individual level. At population level, this new approach enables one to investigate the prevalence of symptoms across different cohorts of patients. At individual level, the new approach enables one to identify patients with poor QoV over time. This study highlights some of the potential shortcomings associated with the current use of Rasch analysis to validate questionnaires. [ABSTRACT FROM AUTHOR]

Identifying the abnormally high-risk regions in a spatiotemporal space that contains an unexpected disease count is helpful to conduct surveillance and implement control strategies. The EigenSpot algorithm has been recently proposed for detecting space-time disease clusters of arbitrary shapes with no restriction on the distribution and quality of the data, and has shown some promising advantages over the state-of-the-art methods. However, the main problem with the EigenSpot method is that it cannot be adapted to detect more than one spatiotemporal hotspot. This is an important limitation, since, in reality, we may have multiple hotspots, sometimes at the same level of importance. We propose an extension of the EigenSpot algorithm, called Multi-EigenSpot that is able to handle multiple hotspots by iteratively removing previously detected hotspots and re-running the algorithm until no more hotspots are found. In addition, a visualization tool (heatmap) has been linked to the proposed algorithm to visualize multiple clusters with different colors. We evaluated the proposed method using the monthly data on measles cases in Khyber-Pakhtunkhwa, Pakistan (Jan 2016- Dec 2016), and the efficiency was compared with the state-of-the-art methods: EigenSpot and Space-time scan statistic (SaTScan). The results showed the effectiveness of the proposed method for detecting multiple clusters in a spatiotemporal space. [ABSTRACT FROM AUTHOR]

In a Perspective, David Peiris and Dorairaj Prabhakaran discuss implications and challenges of cardiovascular disease risk assessments in the population of India. [ABSTRACT FROM AUTHOR]

Back pain is a serious medical problem especially for those people sitting over long periods during their daily work. Here we present a system to help users monitoring and examining their sitting behavior. The Virtual-Spine Platform (VSP) is an integrated system consisting of a real-time body position monitoring module and a data visualization module to provide individualized, immediate, and accurate sitting behavior support. It provides a comprehensive spine movement analysis as well as accumulated data visualization to demonstrate behavior patterns within a certain period. The two modules are discussed in detail focusing on the design of the VSP system with adequate capacity for continuous monitoring and a web-based interactive data analysis method to visualize and compare the sitting behavior of different persons. The data was collected in an experiment with a small group of subjects. Using this method, the behavior of five subjects was evaluated over a working day, enabling inferences and suggestions for sitting improvements. The results from the accumulated data module were used to elucidate the basic function of body position recognition of the VSP. Finally, an expert user study was conducted to evaluate VSP and support future developments. [ABSTRACT FROM AUTHOR]