High Rate Of Misclassification Research Articles

Deep Recurrent Neural Network Assisted Stress Detection System for Working Professionals

Predicting the stress levels of working professionals is one of the most time-consuming and difficult research topics of current day. As a result, estimating working professionals’ stress levels is critical in order to assist them in growing and developing professionally. Numerous machine learning and deep learning algorithms have been developed for this purpose in previous papers. They do, however, have some disadvantages, including increased design complexity, a high rate of misclassification, a high rate of errors, and decreased efficiency. To address these concerns, the purpose of this research is to forecast the stress levels of working professionals using a sophisticated deep learning model called the Deep Recurrent Neural Network (DRNN). The model proposed here comprises dataset preparation, feature extraction, optimal feature selection, and classification using DRNNs. Preprocessing the original dataset removes duplicate attributes and fills in missing values.

Detection of Early Bruises in Honey Peaches Using Shortwave Infrared Hyperspectral Imaging

Honey peaches can bruise during harvesting, handling, storage, transportation, and distribution. In this study, the spectral range used was 400–1100 nm, and we extracted the RGB and HSI color space characteristics of the images. After principal component analysis (PCA) of the original data, the gray histogram features of the PC1 images were extracted. Partial least squares qualitative discriminant analysis (PLS-DA) and extreme learning machine (ELM) discriminant models were established. Among the 38 color features, the PLS-DA and ELM models had a high rate of misclassification, and the best classification accuracy was 74.29%. When extracting the spectral information of the bruised sample to build the model, the highest classification accuracy was 92.86% for the 176 characteristic wavelength points of the full band. In contrast, only 40 wavelength bands were used after selecting the genetic algorithm’s valid information. The classification accuracy of the PLS-DA model was 100%, which is because the softening and browning of the peach was not apparent after early bruising. However, the changes in the tissue’s thermal properties caused by internal defects are expressed in the internal spectrum. Therefore, the shortwave NIR hyperspectral imaging technique’s spectral information can detect the early bruising of peaches.

5G Cognitive Radio Networks Using Reliable Hybrid Deep Learning Based on Spectrum Sensing

Spectrum sensing is critical in allowing the cognitive radio network, which will be used in the next generation of wireless communication systems. Several approaches, including cyclostationary process, energy detectors, and matching filters, have been suggested over the course of several decades. These strategies, on the other hand, have a number of disadvantages. Energy detectors have poor performance when the signal-to-noise ratio (SNR) is changing, cyclostationary detectors are very complicated, and matching filters need previous knowledge of the main user (PU) signals. Additionally, these strategies rely on thresholds under particular signal-noise model assumptions in addition to the thresholds, and as a result, the detection effectiveness of these techniques is wholly dependent on the accuracy of the sensor. In this way, one of the most sought-after difficulties among wireless researchers continues to be the development of a reliable and intelligent spectrum sensing technology. In contrast, multilayer learning models are not ideal for dealing with time-series data because of the large computational cost and high rate of misclassification associated with them. For this reason, the authors propose a hybrid combination of long short-term memory (LSTM) and extreme learning machines (ELM) to learn temporal features from spectral data and to exploit other environmental activity statistics such as energy, distance, and duty cycle duration for the improvement of sensing performance. The suggested system has been tested on a Raspberry Pi Model B+ and the GNU-radio experimental testbed, among other platforms.

Visceral Adiposity in Relation to Body Adiposity and Nutritional Status in Elderly Patients with Stable Coronary Artery Disease.

Introduction: The accumulation of visceral abdominal tissue (VAT) seems to be a hallmark feature of abdominal obesity and substantially contributes to metabolic abnormalities. There are numerous factors that make the body-mass index (BMI) a suboptimal measure of adiposity. The visceral adiposity index (VAI) may be considered a simple surrogate marker of visceral adipose tissue dysfunction. However, the evidence comparing general to visceral adiposity in CAD is scarce. Therefore, we have set out to investigate visceral adiposity in relation to general adiposity in patients with stable CAD. Material and methods: A total of 204 patients with stable CAD hospitalized in the Department of Medicine and the Department of Geriatrics entered the study. Based on the VAI-defined adipose tissue dysfunction (ATD) types, the study population (N = 204) was divided into four groups: (1) no ATD (N = 66), (2) mild ATD (N = 50), (3) moderate ATD (N = 48), and (4) severe ATD (N = 40). Nutritional status was assessed using the Controlling Nutritional Status (CONUT) score. Results: Patients with moderate and severe ATD were the youngest (median 67 years), yet their metabolic age was the oldest (median 80 and 84 years, respectively). CONUT scores were similar across all four study groups. The VAI had only a modest positive correlation with BMI (r = 0.59 p < 0.01) and body adiposity index (BAI) (r = 0.40 p < 0.01). There was no correlation between VAI and CONUT scores. There was high variability in the distribution of BMI-defined weight categories across all four types of ATD. A total of 75% of patients with normal nutritional status had some form of ATD, and one-third of patients with moderate or severe malnutrition did not have any ATD (p = 0.008). In contrast, 55–60% of patients with mild, moderate, or severe ATD had normal nutritional status (p = 0.008). ROC analysis demonstrated that BMI and BAI have poor predictive value in determining no ATD. Both BMI (AUC 0.78 p < 0.0001) and BAI (AUC 0.66 p = 0.003) had strong predictive value for determining severe ATD (the difference between AUC 0.12 being p = 0.0002). However, BMI predicted mild ATD and severe ATD better than BAI. Conclusions: ATD and malnutrition were common in patients with CAD. Notably, this study has shown a high rate of misclassification of visceral ATD via BMI and BAI. In addition, we demonstrated that the majority of patients with normal nutritional status had some form of ATD and as much as one-third of patients with moderate or severe malnutrition did not have any ATD. These findings have important clinical ramifications for everyday practice regarding the line between health and disease in the context of malnutrition in terms of body composition and visceral ATD, which are significant for developing an accurate definition of the standards for the intensity of clinical interventions.

Classifying wakes produced by self-propelled fish-like swimmers using neural networks

ABSTRACT We consider the classification of wake structures produced by self-propelled fish-like swimmers based on local measurements of flow variables. This problem is inspired by the extraordinary capability of animal swimmers in perceiving their hydrodynamic environments under dark condition. We train different neural networks to classify wake structures by using the streamwise velocity component, the crosswise velocity component, the vorticity and the combination of three flow variables, respectively. It is found that the neural networks trained using the two velocity components perform well in identifying the wake types, whereas the neural network trained using the vorticity suffers from a high rate of misclassification. When the neural network is trained using the combination of all three flow variables, a remarkably high accuracy in wake classification can be achieved. The results of this study can be helpful to the design of flow sensory systems in robotic underwater vehicles.

High rate of misclassification of fibrosis stage using Transient Elastography in patients with Primary Biliary Cholangitis

Objective: Liver biopsy is no longer considered necessary for the diagnosis of primary biliary cholangitis (PBC) according to the current EASL guideline. Noninvasive staging of fibrosis by transient elastography (TE by FibroScanâ) has been evaluated in this setting, a TE cut-off value of 9.6 kPa being related with a high risk of outcome events (1). We aimed to assess the reliability of TE in comparison to biopsy in discriminating the stage of PBC at the time of diagnosis.

Human Fall Detection using Accelerometer and Gyroscope Sensors in Unconstrained Smartphone Positions

This study explored several methods for detecting body falls based on the data captured by the sensors (accelerometer and gyroscope) built in a smartphone carried by a person. The data for this study were collected by recording many sample units from each of the following human activity categories: stand-fall, walk-fall, stand-jump, stand-sit, stand, and walk. Several time-series data captured by the sensors were used as human motion features. One of the challenges of this study was the existence of human body motions whose features resembled those of body falls. In addition, unfixed smartphone positioning made human body falls harder to detect and can lead to high rate of misclassification (not detected as fall). This incident can caused serious bone fracture or even death if the person not handled as immediately as possible because of misclassification. To address this problem, we modified Resultant Acceleration and ∠Y formulas to address the problem of unconstrained smartphone positions. We proposed to combine five methods such as AGVeSR, Alim, ∠α, GyroReDi, and AGPeak to build a robust detector model to reduce the misclassification. The experiment results showed that the accuracy of the combination of both sensors (accelerometer and gyroscope) outperformed the accuracy of accelerometer only by more than 15%. The decision fusion that used voting involving five methods could boost the accuracy rate by up to 4.15%.

295 Validation of a Cytotechnologist Manual Counting Service for the Ki67 Index in Neuroendocrine Tumors of the Pancreas and Gastrointestinal Tract

Pathologists routinely assess Ki67 immunohistochemistry to grade gastrointestinal and pancreatic neuroendocrine tumors. Unfortunately, manual counts of the Ki67 index are very time consuming, and “eyeball estimation” has been criticized for being unreliable. Manual Ki67 counts performed by cytotechnologists could potentially save pathologist time and improve accuracy. To assess the concordance between manual Ki67 index counts performed by cytotechnologists vs eyeball estimates and manual Ki67 counts by pathologists. Ki67 immunohistochemical stains were retrieved from archived gastrointestinal or pancreatic neuroendocrine tumor resections. We compared pathologist Ki67 eyeball estimates on glass slides and printed color images with manual counts by three cytotechnologists and gold standard manual Ki67 index counts by three pathologists. Tumor grade agreement between pathologist image eyeball estimate and gold standard pathologist manual count was fair (K value of 0.31 [95% CI 0.03–0.60]). In nine of 20 cases (45%), the mean pathologist eyeball estimate was one grade higher than the mean pathologist manual count. There was almost perfect agreement in classifying tumor grade between the mean cytotechnologist manual count and the mean pathologist manual count (K value of 0.91 [95% CI 0.7–1.0]). In 20 cases, there was only one grade disagreement between the two methods. Eyeball estimation by pathologists required less than one minute, while manual counts by pathologists required a mean of 17 minutes per case. Eyeball estimation of the Ki67 index has a high rate of tumor grade misclassification compared to manual counting. Cytotechnologist manual counts are accurate and save pathologists time.

Validation of a Cytotechnologist Manual Counting Service for the Ki67 Index in Neuroendocrine Tumors of the Pancreas and Gastrointestinal Tract.

- Pathologists routinely assess Ki67 immunohistochemistry to grade gastrointestinal and pancreatic neuroendocrine tumors. Unfortunately, manual counts of the Ki67 index are very time consuming and eyeball estimation has been criticized as unreliable. Manual Ki67 counts performed by cytotechnologists could potentially save pathologist time and improve accuracy. - To assess the concordance between manual Ki67 index counts performed by cytotechnologists versus eyeball estimates and manual Ki67 counts by pathologists. - One Ki67 immunohistochemical stain was retrieved from each of 18 archived gastrointestinal or pancreatic neuroendocrine tumor resections. We compared pathologists' Ki67 eyeball estimates on glass slides and printed color images with manual counts performed by 3 cytotechnologists and gold standard manual Ki67 index counts by 3 pathologists. - Tumor grade agreement between pathologist image eyeball estimate and gold standard pathologist manual count was fair (κ = 0.31; 95% CI, 0.030-0.60). In 9 of 20 cases (45%), the mean pathologist eyeball estimate was 1 grade higher than the mean pathologist manual count. There was almost perfect agreement in classifying tumor grade between the mean cytotechnologist manual count and the mean pathologist manual count (κ = 0.910; 95% CI, 0.697-1.00). In 20 cases, there was only 1 grade disagreement between the 2 methods. Eyeball estimation by pathologists required less than 1 minute, whereas manual counts by pathologists required a mean of 17 minutes per case. - Eyeball estimation of the Ki67 index has a high rate of tumor grade misclassification compared with manual counting. Cytotechnologist manual counts are accurate and save pathologist time.

A hybrid intrusion detection system (HIDS) based on prioritized k-nearest neighbors and optimized SVM classifiers

Intrusion Detection System (IDS) is an effective security tool that helps preventing unauthorized access to network resources through analyzing the network traffic. However, due to the large amount of data flowing over the network, effective real time intrusion detection is almost impossible. The goal of this paper is to design a Hybrid IDS (HIDS) that can be successfully employed in a real time manner and suitable for resolving the multi-class classification problem. HIDS relies on a Naive Base feature selection (NBFS) technique, which is used to reduce the dimensionality of sample data. Moreover, HIDS has another pioneering issue that other techniques do not have, which is the outlier rejection. Outliers are noisy input samples that can lead to high rate of misclassification if they are applied for model training. Rejecting outliers has been accomplished through applying a distance based methodology to choose the most informative training examples, which are then used to train an Optimized Support Vector Machines (OSVM). Afterward, OSVM is employed for rejecting outliers. Finally, after outlier rejection, HIDS can successfully detect attacks through applying a Prioritized K-Nearest Neighbors (PKNN) classifier. Hence, HIDS is a triple edged strategy as it has three main contributions, which are: (i) NBFS, which has been employed for dimensionality reduction, (ii) OSVM, which is applied for outlier rejection, and (iii) PKNN, which is used for detecting input attacks. HIDS has been compared against recent techniques using three well-known intrusion detection datasets: KDD Cup ’99, NSL-KDD and Kyoto 2006+ datasets. HIDS has the ability to quickly detect attacks and accordingly can be employed for real time intrusion detection. Thanks to OSVM and PKNN, HIDS performed high detection rates specifically for the attacks which are rare such as R2L and U2R. PKNN is also suitable for resolving the multi-label classification problem.

New Discrimination Procedure of Location Model for Handling Large Categorical Variables

The location model proposed in the past is a predictive discriminant rule that can classify new observations into one of two predefined groups based on mixtures of continuous and categorical variables. The ability of location model to discriminate new observation correctly is highly dependent on the number of multinomial cells created by the number of categorical variables. This study conducts a preliminary investigation to show the location model that uses maximum likelihood estimation has high misclassification rate up to 45% on average in dealing with more than six categorical variables for all 36 data tested. Such model indicated highly incorrect prediction as this model performed badly for large categorical variables even with large sample size. To alleviate the high rate of misclassification, a new strategy is embedded in the discriminant rule by introducing nonlinear principal component analysis (NPCA) into the classical location model (cLM), mainly to handle the large number of categorical variables. This new strategy is investigated on some simulation and real datasets through the estimation of misclassification rate using leave-one-out method. The results from numerical investigations manifest the feasibility of the proposed model as the misclassification rate is dramatically decreased compared to the cLM for all 18 different data settings. A practical application using real dataset demonstrates a significant improvement and obtains comparable result among the best methods that are compared. The overall findings reveal that the proposed model extended the applicability range of the location model as previously it was limited to only six categorical variables to achieve acceptable performance. This study proved that the proposed model with new discrimination procedure can be used as an alternative to the problems of mixed variables classification, primarily when facing with large categorical variables.

Accuracy of self-reported smoking status in adult survivors of childhood cancer: A report from St. Jude Lifetime Cohort study.

10570 Background: Although clinicians often evaluate smoking behavior in cancer survivors via self-report, the validity of this approach is unknown. We validated self-reported smoking status by serum cotinine data in clinically assessed adult survivors of childhood cancer and identified factors contributing to misclassification. Methods: The study sample consists of 287 randomly selected adult survivors of childhood cancers participating in the St. Jude Lifetime Cohort Study and undergoing a risk-based clinical assessment in a survivorship clinic. Self-reported smoking status was classified as never (N = 105), past (N = 111), and current (N = 71) smokers. Age, sex, and race/ethnicity were balanced among the three groups (p’s &gt; 0.05). Blood samples were obtained and serum cotinine levels were quantified by liquid chromatography tandem mass spectrometry. Misclassification was determined by the discrepancies between self-reported smoking status and race/ethnicity-specific serum cotinine thresholds (Benowitz et al. Am J Epidemiol 2009). Multiple logistic regression model was used to identify factors related to misclassification. Results: Of the 287 survivors (mean age = 34 years [range = 19-61]; mean time from diagnosis = 24 years [range = 11-46]), 55.4% were male and 84.0% non-Hispanic white. Cotinine levels consistent with recent active smoking were present in 39.4% compared to 24.7% who self-reported as being a current smoker (X2= 14.1; p = 0.0002). Rates of misclassification were 36.9%, 8.4%, and 6.7% in survivors who reported themselves as past, current, and never smokers, respectively. Among self-reported past smokers, 18-30 years of age at survey, male, and current marijuana use increased the risk of misclassification: RR = 3.0 (95%CI = 1.2-7.6), 2.5 (95%CI = 1.1-5.4) and 3.2 (95%CI = 1.1-9.3), respectively. Conclusions: Within a clinical setting, reliance on self-report of smoking status by survivors results in a high misclassification rate. For research, serum cotinine levels should be utilized to assign smoking status. For clinical care and health promotion, clinicians need to be aware of the high rate of misclassification when relying upon self-reported smoking status.

PD47-10 THE RESEARCH IMPLICATIONS OF PSA REGISTRY ERRORS

You have accessJournal of UrologyProstate Cancer: Epidemiology & Natural History III1 Apr 2017PD47-10 THE RESEARCH IMPLICATIONS OF PSA REGISTRY ERRORS David Guo, I-Chung Thomas, Harsha Mittakanti, Jeremy Shelton, Danil Makarov, Ted Skolarus, Matthew Cooperberg, Geoffrey Sonn, Benjamin Chung, James Brooks, and John Leppert David GuoDavid Guo More articles by this author , I-Chung ThomasI-Chung Thomas More articles by this author , Harsha MittakantiHarsha Mittakanti More articles by this author , Jeremy SheltonJeremy Shelton More articles by this author , Danil MakarovDanil Makarov More articles by this author , Ted SkolarusTed Skolarus More articles by this author , Matthew CooperbergMatthew Cooperberg More articles by this author , Geoffrey SonnGeoffrey Sonn More articles by this author , Benjamin ChungBenjamin Chung More articles by this author , James BrooksJames Brooks More articles by this author , and John LeppertJohn Leppert More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2017.02.2370AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Errors in prostate specific antigen (PSA) values included in prostate cancer registries have called into question clinical research studies that rely on this information. We sought to characterize the potential effects of PSA registry errors on clinical research by comparing cohorts based on registry PSA values with those based on laboratory values extracted from an integrated national health care system. METHODS We defined three example cohorts of men with prostate cancer using data from the VA integrated health care system: those with ″very low″ (<4.0 ng/mL), ″low″ (<10.0 ng/mL), and ″high″ (20-100 ng/mL) PSA values. We compared the composition of each cohort when using the cancer registry versus the electronic health record PSA values. We compared overall survival for each cohort as an example clinical outcome. We fit multivariable proportional hazards models to determine the importance of the PSA source in each cohort. RESULTS There was significant discordance when using cancer registry versus electronic health record PSA values to identify a cohort of patients with ″very low PSA″ values. While 7,286 were included in both cohorts, one third (n=3,515) of the cohort defined using cancer registry PSA values was misclassified and 1,800 additional patients were identified when using electronic health record data. The concordance was highest for patients with ″low″ PSA values, with 21,860 (98%) of patients identified in both the cancer registry and electronic health record based cohorts. Cancer registry PSA values misclassified 41% (604) of the ″high″ PSA cohort, and 133 additional patients were identified using electronic health record data. Comparisons of overall survival in the examples cohorts identified a difference in overall survival in the ″very low″ (log rank P=0.03), but not the ″low″ or ″high″ PSA cohorts. CONCLUSIONS Patient cohorts based on cancer registry PSA values may have high rate of misclassification, particularly among patients with ″very low″ or ″high″ PSA values. In some cases, differences in cohorts resulted in measurable differences in overall survival. Attempts should be made to validate cancer registry PSA data to ensure accurate and reproducible results. © 2017FiguresReferencesRelatedDetails Volume 197Issue 4SApril 2017Page: e899-e900 Advertisement Copyright & Permissions© 2017MetricsAuthor Information David Guo More articles by this author I-Chung Thomas More articles by this author Harsha Mittakanti More articles by this author Jeremy Shelton More articles by this author Danil Makarov More articles by this author Ted Skolarus More articles by this author Matthew Cooperberg More articles by this author Geoffrey Sonn More articles by this author Benjamin Chung More articles by this author James Brooks More articles by this author John Leppert More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

High rate of misclassification of fibrosis stage using transient elastography thresholds to prioritize HCV patients for antiviral treatment

High rate of misclassification of fibrosis stage using transient elastography thresholds to prioritize HCV patients for antiviral treatment

The agreement between abnormal venous lactate and arterial lactate in the ED: a retrospective chart review

IntroductionThe evidence for prognostication using lactate is often based on arterial lactate (AL). Arterial sampling is painful and difficult, and carries risks. Studies comparing peripheral venous lactate (PVL) with AL showed little difference but predominantly included patients with normal lactate. The objective of this study was to measure agreement between PVL and AL in patients with elevated venous lactate. MethodsThis is a retrospective cross-sectional study. Inclusion criteria: ED patients age ≥16, attending from October 2010 to June 2011 inclusive, with PVL ≥2.0mmol/L and AL taken within 1 hour. Exclusion criteria: intravenous fluid prior to or between initial venous and arterial sampling. Primary endpoint: agreement between PVL and AL defined as mean difference ±95% limits of agreement (LOA). The misclassification rate was assessed. ResultsN=232. VL median 3.50mmol/L, range 2.00 to 15.00mmol/L. AL median 2.45mmol/L, range 1.0 to 13.2mmol/L. The mean difference±SD between PVL and AL for all patients was 1.06±1.30mmol/L (95%LOA −1.53 to 3.66mmol/L). Using a cut-off of 2mmol/L and 4mmol/L, 36.2% and 17.9% of patients respectively were incorrectly classified as having elevated lactate. ConclusionWe report greater bias between VL and AL with broader LOA than previously documented. This may partly be due to the fact that we studied only patients with abnormal venous values, for whom close agreement would confer greatest clinical significance. The agreement between abnormal PVL and AL is poor and the high rate of misclassification may suggest that PVL is not a good substitute for AL if the venous lactate is abnormal.

Can nurses successfully diagnose and manage patients with COPD?

Can nurses successfully diagnose and manage patients with COPD?

Novel Approach for Rooftop Detection Using Support Vector Machine

A new method for detecting rooftops in satellite images is presented. The proposed method is based on a combination of machine learning techniques, namely, k-means clustering and support vector machines (SVM). Firstly k-means clustering is used to segment the image into a set of rooftop candidates—these are homogeneous regions in the image which are potentially associated with rooftop areas. Next, the candidates are submitted to a classification stage which determines which amongst them correspond to “true” rooftops. To achieve improved accuracy, a novel two-pass classification process is used. In the first pass, a trained SVM is used in the normal way to distinguish between rooftop and nonrooftop regions. However, this can be a challenging task, resulting in a relatively high rate of misclassification. Hence, the second pass, which we call the “histogram method,” was devised with the aim of detecting rooftops which were missed in the first pass. The performance of the model is assessed both in terms of the percentage of correctly classified candidates as well as the accuracy of the estimated rooftop area.

Differential Specificity of HIV Incidence Assays in HIV Subtypes A and D-Infected Individuals from Rakai, Uganda

Assays to determine HIV incidence from cross-sectional surveys have exhibited a high rate of false-recent misclassification in Kenya and Uganda where HIV subtypes A and D predominate. Samples from individuals infected with HIV for at least 2 years with known infecting subtype (133 subtype A, 373 subtype D) were tested using the BED-CEIA and an avidity assay. Both assays had a higher rate of false-recent misclassification for subtype D compared to subtype A (13.7% vs. 6.0%, p=0.02 for BED-CEIA; 11.0% vs. 1.5%, p<0.001 for avidity). For subtype D samples, false-recent misclassification by the BED-CEIA was also more frequent in women than men (15.0% vs. 5.6%, p=0.002), and for samples where that had an amino acid other than lysine at position 12 in the BED-CEIA peptide coding region (p=0.002). Furthermore in subtype D-infected individuals, samples misclassified by one assay were 3.5 times more likely to be misclassified by the other assay. Differential misclassification by infecting subtype of long-term infected individuals as recently infected makes it difficult to use these assays individually to estimate population level incidence without precise knowledge of the distribution of these subtypes within populations where subtype A and D cocirculate. The association of misclassification of the BED-CEIA with the avidity assay in subtype D-infected individuals limits the utility of using these assays in combination within this population.

067 COMPARATIVE ACCURACY OF STANDARD ECG ALGORITHMS FOR THE DIAGNOSIS OF BROAD COMPLEX TACHYCARDIA IN CASES OF ATRIAL FLUTTER WITH 1:1 CONDUCTION:

<h3>Background</h3> Atrial flutter with 1:1 conduction often presents with broad complex tachycardia (BCT), commonly misdiagnosed as ventricular tachycardia (VT). The utility of standard ECG algorithms for classification of BCT has not been evaluated in this subgroup. <h3>Methods</h3> 25 patients had confirmed atrial flutter with 1:1 conduction presenting as BCT. 12/25 (48%) were taking class I agents. The 12-lead ECGs were independently classified as ‘SVT’ or ‘VT’ by two blinded electrophysiologists, using five established BCT algorithms: Wellens <i>et al</i> 1978; Brugada <i>et al</i> 1991; Griffith <i>et al</i> 1994; Vereckei <i>et al</i> 2008; Pava <i>et al</i> 2010. <h3>Results</h3> Mean ventricular rate 219±34 bpm, LBBB pattern in 19/25 (76%), QRS duration &gt;140 ms in 20/25 (80%) and left axis deviation in 14/25 (56%) patients. Possible (but not definite) VA-dissociation was noted in 10 cases (40%).There was close inter-observer agreement. The results of the ECG analysis were: There was a high rate of mis-classification as VT by all algorithms except Pava (R wave peak time at DII or RWPT) which correctly identified the origin in 76% of cases. <h3>Conclusions</h3> Atrial flutter presenting with 1:1 conduction and BCT is often mistaken for VT and ‘pseudo-VA dissociation’ is relatively common. Standard ECG algorithms are relatively inaccurate in this subgroup, except for RWPT which identified the supraventricular origin in 76% of cases. A high level of vigilance is necessary to make the correct diagnosis

Evaluation of Ara h2 IgE thresholds in the diagnosis of peanut allergy in a clinical population

Evaluation of Ara h2 IgE thresholds in the diagnosis of peanut allergy in a clinical population