BACKGROUND: A test panel is a group of tests that are simultaneously performed for diagnosis and follow-up of patients. Organ-specific or disease-specific test panels are currently available. Since the patient's chief complaint plays a key role in obtaining the personal and medical history and performing physical examinations, we proposed a test panel based on the chief complaints of the patients. METHODS: We collected data from 3,127 adults with apparent symptoms who visited the emergency department from April 2009 to May 2009. Subsequently, we classified the patients' chief complaints, ordered the laboratory tests on the basis of these complaints, considered the patients' disease entities, and reviewed the relevant literature. RESULTS: The patients were categorized into 14 groups on the basis of the most common chief complaints presented in the emergency department. We first selected the basic test panels and then organized the test panel for each chief complaint to enable differential diagnosis. CONCLUSIONS: We proposed test panels based on the chief complaints of the patients; these test panels could allow rapid diagnosis and be more useful than the organ-specific or disease-specific tests in critical pathway development. The next step will be evaluating the efficiency and cost effectiveness of the test panel that we suggested.
Diagnostic Tests, Routine ; *Emergency Service, Hospital/statistics & numerical data ; Humans ; *Laboratory Techniques and Procedures ; Primary Health Care

Meta-analysis of diagnostic test accuracy studies differs from the usual meta-analysis of therapeutic/interventional studies in that, it is required to simultaneously analyze a pair of two outcome measures such as sensitivity and specificity, instead of a single outcome. Since sensitivity and specificity are generally inversely correlated and could be affected by a threshold effect, more sophisticated statistical methods are required for the meta-analysis of diagnostic test accuracy. Hierarchical models including the bivariate model and the hierarchical summary receiver operating characteristic model are increasingly being accepted as standard methods for meta-analysis of diagnostic test accuracy studies. We provide a conceptual review of statistical methods currently used and recommended for meta-analysis of diagnostic test accuracy studies. This article could serve as a methodological reference for those who perform systematic review and meta-analysis of diagnostic test accuracy studies.
Area Under Curve ; Databases, Factual ; Diagnostic Tests, Routine/*statistics & numerical data ; Humans ; ROC Curve ; *Research ; Software

The aim of this study was to estimate the benefit from repeated examinations in the diagnosis of enterobiasis in nursery school groups, and to test the effectiveness of individual-based risk predictions using different methods. A total of 604 children were examined using double, and 96 using triple, anal swab examinations. The questionnaires for parents, structured observations, and interviews with supervisors were used to identify factors of possible infection risk. In order to model the risk of enterobiasis at individual level, a similarity-based machine learning and prediction software Constud was compared with data mining methods in the Statistica 8 Data Miner software package. Prevalence according to a single examination was 22.5%; the increase as a result of double examinations was 8.2%. Single swabs resulted in an estimated prevalence of 20.1% among children examined 3 times; double swabs increased this by 10.1%, and triple swabs by 7.3%. Random forest classification, boosting classification trees, and Constud correctly predicted about 2/3 of the results of the second examination. Constud estimated a mean prevalence of 31.5% in groups. Constud was able to yield the highest overall fit of individual-based predictions while boosting classification tree and random forest models were more effective in recognizing Enterobius positive persons. As a rule, the actual prevalence of enterobiasis is higher than indicated by a single examination. We suggest using either the values of the mean increase in prevalence after double examinations compared to single examinations or group estimations deduced from individual-level modelled risk predictions.
Anal Canal/parasitology ; Animals ; *Diagnostic Tests, Routine/methods ; Enterobiasis/*diagnosis/epidemiology ; Enterobius/*isolation & purification ; Estonia/epidemiology ; Female ; Humans ; Male ; Prevalence ; *Schools, Nursery/statistics & numerical data

Chromosomal microarray analysis (CMA) enables the genome-wide detection of submicroscopic chromosomal imbalances with greater precision and accuracy. In most other countries, CMA is now a commonly used clinical diagnostic test, replacing conventional cytogenetics or targeted detection such as FISH or PCR-based methods. Recently, some consensus statements have proposed utilization of CMA as a first-line test in patients with multiple congenital anomalies not specific to a well-delineated genetic syndrome, developmental delay/intellectual disability, or autism spectrum disorders. CMA can be used as an adjunct to conventional cytogenetics to identify chromosomal abnormalities observed in G-banding analysis in constitutional or acquired cases, leading to a more accurate and comprehensive assessment of chromosomal aberrations. Although CMA has distinct advantages, there are several limitations, including its inability to detect balanced chromosomal rearrangements and low-level mosaicism, its interpretation of copy number variants of uncertain clinical significance, and significantly higher costs. For these reasons, CMA is not currently a replacement for conventional cytogenetics in prenatal diagnosis. In clinical applications of CMA, knowledge and experience based on genetics and cytogenetics are required for data analysis and interpretation, and appropriate follow-up with genetic counseling is recommended.
Child ; Autism Spectrum Disorder ; Chromosome Aberrations ; Coat Protein Complex I ; Consensus ; Cytogenetics ; Diagnostic Tests, Routine ; Genetic Counseling ; Genomic Structural Variation ; Humans ; Microarray Analysis ; Mosaicism ; Prenatal Diagnosis ; Statistics as Topic