153 research outputs found

    Case Based Reasoning Untuk Mendiagnosa Penyakit Anak Menggunakan Metode Block City

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    The Case Based Reasoning (CBR) method is one of the methods to build a system that works by diagnosing new cases based on old cases that have occurred and providing solutions to new cases based on old cases with the highest similarity values. In this study, the authors apply CBR to diagnose diseases of children aged 1-12 years. Sources of system knowledge were obtained by collecting patient medical record files in 2014 and 2015. The calculation of similarity values using the Block City Gower method with a fairness value is 70%. This system can diagnose 10 illnesses based on 48 existing symptoms. The output of the system in the form of the illness experienced by the patient based on symptoms implanted by non-physician medical personnel, handling solution and presentation similarities with the previous case to show the truth level of the diagnosis. Based on the test of 83 new cases obtained system accuracy of 75,90%

    Further investigation of confirmed urinary tract infection (UTI) in children under five years: a systematic review.

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    Background: Further investigation of confirmed UTI in children aims to prevent renal scarring and future complications. Methods: We conducted a systematic review to determine the most effective approach to the further investigation of confirmed urinary tract infection (UTI) in children under five years of age. Results: 73 studies were included. Many studies had methodological limitations or were poorly reported. Effectiveness of further investigations: One study found that routine imaging did not lead to a reduction in recurrent UTIs or renal scarring. Diagnostic accuracy: The studies do not support the use of less invasive tests such as ultrasound as an alternative to renal scintigraphy, either to rule out infection of the upper urinary tract (LR- = 0.57, 95%CI: 0.47, 0.68) and thus to exclude patients from further investigation or to detect renal scarring (LR+ = 3.5, 95% CI: 2.5, 4.8). None of the tests investigated can accurately predict the development of renal scarring. The available evidence supports the consideration of contrast-enhanced ultrasound techniques for detecting vesico-ureteric reflux (VUR), as an alternative to micturating cystourethrography (MCUG) (LR+ = 14.1, 95% CI: 9.5, 20.8; LR- = 0.20, 95%CI: 0.13, 0.29); these techniques have the advantage of not requiring exposure to ionising radiation. Conclusion: There is no evidence to support the clinical effectiveness of routine investigation of children with confirmed UTI. Primary research on the effectiveness, in terms of improved patient outcome, of testing at all stages in the investigation of confirmed urinary tract infection is urgently required

    How does study quality affect the results of a diagnostic meta-analysis?

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    Background: The use of systematic literature review to inform evidence based practice in diagnostics is rapidly expanding. Although the primary diagnostic literature is extensive, studies are often of low methodological quality or poorly reported. There has been no rigorously evaluated, evidence based tool to assess the methodological quality of diagnostic studies. The primary objective of this study was to determine the extent to which variations in the quality of primary studies impact the results of a diagnostic meta-analysis and whether this differs with diagnostic test type. A secondary objective was to contribute to the evaluation of QUADAS, an evidence-based tool for the assessment of quality in diagnostic accuracy studies. Methods: This study was conducted as part of large systematic review of tests used in the diagnosis and further investigation of urinary tract infection (UTI) in children. All studies included in this review were assessed using QUADAS, an evidence-based tool for the assessment of quality in systematic reviews of diagnostic accuracy studies. The impact of individual components of QUADAS on a summary measure of diagnostic accuracy was investigated using regression analysis. The review divided the diagnosis and further investigation of UTI into the following three clinical stages: diagnosis of UTI, localisation of infection, and further investigation of the UTI. Each stage used different types of diagnostic test, which were considered to involve different quality concerns. Results: Many of the studies included in our review were poorly reported. The proportion of QUADAS items fulfilled was similar for studies in different sections of the review. However, as might be expected, the individual items fulfilled differed between the three clinical stages. Regression analysis found that different items showed a strong association with test performance for the different tests evaluated. These differences were observed both within and between the three clinical stages assessed by the review. The results of regression analyses were also affected by whether or not a weighting (by sample size) was applied. Our analysis was severely limited by the completeness of reporting and the differences between the index tests evaluated and the reference standards used to confirm diagnoses in the primary studies. Few tests were evaluated by sufficient studies to allow meaningful use of meta-analytic pooling and investigation of heterogeneity. This meant that further analysis to investigate heterogeneity could only be undertaken using a subset of studies, and that the findings are open to various interpretations. Conclusion: Further work is needed to investigate the influence of methodological quality on the results of diagnostic meta-analyses. Large data sets of well-reported primary studies are needed to address this question. Without significant improvements in the completeness of reporting of primary studies, progress in this area will be limited

    No role for quality scores in systematic reviews of diagnostic accuracy studies

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    BACKGROUND: There is a lack of consensus regarding the use of quality scores in diagnostic systematic reviews. The objective of this study was to use different methods of weighting items included in a quality assessment tool for diagnostic accuracy studies (QUADAS) to produce an overall quality score, and to examine the effects of incorporating these into a systematic review. METHODS: We developed five schemes for weighting QUADAS to produce quality scores. We used three methods to investigate the effects of quality scores on test performance. We used a set of 28 studies that assessed the accuracy of ultrasound for the diagnosis of vesico-ureteral reflux in children. RESULTS: The different methods of weighting individual items from the same quality assessment tool produced different quality scores. The different scoring schemes ranked different studies in different orders; this was especially evident for the intermediate quality studies. Comparing the results of studies stratified as "high" and "low" quality based on quality scores resulted in different conclusions regarding the effects of quality on estimates of diagnostic accuracy depending on the method used to produce the quality score. A similar effect was observed when quality scores were included in meta-regression analysis as continuous variables, although the differences were less apparent. CONCLUSION: Quality scores should not be incorporated into diagnostic systematic reviews. Incorporation of the results of the quality assessment into the systematic review should involve investigation of the association of individual quality items with estimates of diagnostic accuracy, rather than using a combined quality score

    Global Intraurban Intake Fractions for Primary Air Pollutants from Vehicles and Other Distributed Sources

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    We model intraurban intake fraction (iF) values for distributed ground-level emissions in all 3646 global cities with more than 100,000 inhabitants, encompassing a total population of 2.0 billion. For conserved primary pollutants, population-weighted median, mean, and interquartile range iF values are 26, 39, and 14-52 ppm, respectively, where 1 ppm signifies 1 g inhaled/t emitted. The global mean urban iF reported here is roughly twice as large as previous estimates for cities in the United States and Europe. Intake fractions vary among cities owing to differences in population size, population density, and meteorology. Sorting by size, population-weighted mean iF values are 65, 35, and 15 ppm, respectively, for cities with populations larger than 3, 0.6-3, and 0.1-0.6 million. The 20 worldwide megacities (each >10 million people) have a population-weighted mean iF of 83 ppm. Mean intraurban iF values are greatest in Asia and lowest in land-rich high-income regions. Country-average iF values vary by a factor of 3 among the 10 nations with the largest urban populations

    Confounding and exposure measurement error in air pollution epidemiology

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    Studies in air pollution epidemiology may suffer from some specific forms of confounding and exposure measurement error. This contribution discusses these, mostly in the framework of cohort studies. Evaluation of potential confounding is critical in studies of the health effects of air pollution. The association between long-term exposure to ambient air pollution and mortality has been investigated using cohort studies in which subjects are followed over time with respect to their vital status. In such studies, control for individual-level confounders such as smoking is important, as is control for area-level confounders such as neighborhood socio-economic status. In addition, there may be spatial dependencies in the survival data that need to be addressed. These issues are illustrated using the American Cancer Society Cancer Prevention II cohort. Exposure measurement error is a challenge in epidemiology because inference about health effects can be incorrect when the measured or predicted exposure used in the analysis is different from the underlying true exposure. Air pollution epidemiology rarely if ever uses personal measurements of exposure for reasons of cost and feasibility. Exposure measurement error in air pollution epidemiology comes in various dominant forms, which are different for time-series and cohort studies. The challenges are reviewed and a number of suggested solutions are discussed for both study domains
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