13 research outputs found

    Adaptation of a Vocabulary Test from British Sign Language to American Sign Language

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    This study describes the adaptation process of a vocabulary knowledge test for British Sign Language (BSL) into American Sign Language (ASL) and presents results from the first round of pilot testing with twenty deaf native ASL signers. The web-based test assesses the strength of deaf children’s vocabulary knowledge by means of different mappings of phonological form and meaning of signs. The adaptation from BSL to ASL involved nine stages, which included forming a panel of deaf/hearing experts, developing a set of new items and revising/replacing items considered ineffective, and piloting the new version. Results provide new evidence in support of the use of this methodology for assessing sign language, making a useful contribution toward the availability of tests to assess deaf children’s signed language skills

    Congenital and childhood atrioventricular blocks: pathophysiology and contemporary management

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    Atrioventricular block is classified as congeni- tal if diagnosed in utero, at birth, or within the first month of life. The pathophysiological process is believed to be due to immune-mediated injury of the conduction system, which occurs as a result of transplacental pas- sage of maternal anti-SSA/Ro-SSB/La antibodies. Childhood atrioventricular block is therefore diagnosed between the first month and the 18th year of life. Genetic variants in multiple genes have been described to date in the pathogenesis of inherited progressive car- diac conduction disorders. Indications and techniques of cardiac pacing have also evolved to allow safe perma- nent cardiac pacing in almost all patients, including those with structural heart abnormalities

    Development of a Novel Risk Prediction Model for Sudden Cardiac Death in Childhood Hypertrophic Cardiomyopathy (HCM Risk-Kids)

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    Importance: Sudden cardiac death (SCD) is the most common mode of death in childhood hypertrophic cardiomyopathy (HCM), but there is no validated algorithm to identify those at highest risk. Objective: To develop and validate an SCD risk prediction model that provides individualized risk estimates. Design, Setting, and Participants: A prognostic model was developed from a retrospective, multicenter, longitudinal cohort study of 1024 consecutively evaluated patients aged 16 years or younger with HCM. The study was conducted from January 1, 1970, to December 31, 2017. Exposures: The model was developed using preselected predictor variables (unexplained syncope, maximal left-ventricular wall thickness, left atrial diameter, left-ventricular outflow tract gradient, and nonsustained ventricular tachycardia) identified from the literature and internally validated using bootstrapping. Main Outcomes and Measures: A composite outcome of SCD or an equivalent event (aborted cardiac arrest, appropriate implantable cardioverter defibrillator therapy, or sustained ventricular tachycardia associated with hemodynamic compromise). Results: Of the 1024 patients included in the study, 699 were boys (68.3%); mean (interquartile range [IQR]) age was 11 (7-14) years. Over a median follow-up of 5.3 years (IQR, 2.6-8.3; total patient years, 5984), 89 patients (8.7%) died suddenly or had an equivalent event (annual event rate, 1.49; 95% CI, 1.15-1.92). The pediatric model was developed using preselected variables to predict the risk of SCD. The model's ability to predict risk at 5 years was validated; the C statistic was 0.69 (95% CI, 0.66-0.72), and the calibration slope was 0.98 (95% CI, 0.59-1.38). For every 10 implantable cardioverter defibrillators implanted in patients with 6% or more of a 5-year SCD risk, 1 patient may potentially be saved from SCD at 5 years. Conclusions and Relevance: This new, validated risk stratification model for SCD in childhood HCM may provide individualized estimates of risk at 5 years using readily obtained clinical risk factors. External validation studies are required to demonstrate the accuracy of this model's predictions in diverse patient populations
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