Metyrapone Stimulation Test

Author(s):  
Ahmet Bahadir Ergin ◽  
Amir H. Hamrahian ◽  
A. Laurence Kennedy ◽  
Manjula K. Gupta
Keyword(s):  
2018 ◽  
Vol 24 ◽  
pp. 273-274
Author(s):  
Corin Badiu ◽  
Mara Baet ◽  
Ruxandra Dobrescu ◽  
Andra Caragheorgheopol ◽  
Corneci Cristina

1978 ◽  
Vol 17 (01) ◽  
pp. 16-23 ◽  
Author(s):  
Ch. L. Zollikofer ◽  
J. Wewerka ◽  
Th. Frank

35 patients with scintigraphically silent thyroid regions without palpable cold nodules were further evaluated by ultrasonography. In 33 cases the sonographic diagnosis was confirmed by other examinations or the clinical course. 2 cases were misinterpreted right at the beginning of our series.The use of ultrasonography in evaluating silent thyroid regions in the totally decompensated autonomous adenoma, in unilateral thyroid aplasia, thyroiditis and hyperthyroidism is shown to be a reliable and valuable supplement to the clinical and radioisotopic evaluation procedures. When differentiating the totally decompensated autonomous adenoma from unilateral thyroid aplasia a stimulation test need not be performed in most cases. Suspected thyroiditis can be confirmed in a simple way. Being a non-invasive evaluation procedure, ultrasonography should be used before performing a needle biopsy.


2019 ◽  
Author(s):  
Adriana Albani ◽  
Luis Perez-Rivas ◽  
Michael Buchfelder ◽  
Jurgen Honegger ◽  
Gunter Stalla ◽  
...  

2018 ◽  
Author(s):  
Liyan Pan ◽  
Guangjian Liu ◽  
Xiaojian Mao ◽  
Huixian Li ◽  
Jiexin Zhang ◽  
...  

BACKGROUND Central precocious puberty (CPP) in girls seriously affects their physical and mental development in childhood. The method of diagnosis—gonadotropin-releasing hormone (GnRH)–stimulation test or GnRH analogue (GnRHa)–stimulation test—is expensive and makes patients uncomfortable due to the need for repeated blood sampling. OBJECTIVE We aimed to combine multiple CPP–related features and construct machine learning models to predict response to the GnRHa-stimulation test. METHODS In this retrospective study, we analyzed clinical and laboratory data of 1757 girls who underwent a GnRHa test in order to develop XGBoost and random forest classifiers for prediction of response to the GnRHa test. The local interpretable model-agnostic explanations (LIME) algorithm was used with the black-box classifiers to increase their interpretability. We measured sensitivity, specificity, and area under receiver operating characteristic (AUC) of the models. RESULTS Both the XGBoost and random forest models achieved good performance in distinguishing between positive and negative responses, with the AUC ranging from 0.88 to 0.90, sensitivity ranging from 77.91% to 77.94%, and specificity ranging from 84.32% to 87.66%. Basal serum luteinizing hormone, follicle-stimulating hormone, and insulin-like growth factor-I levels were found to be the three most important factors. In the interpretable models of LIME, the abovementioned variables made high contributions to the prediction probability. CONCLUSIONS The prediction models we developed can help diagnose CPP and may be used as a prescreening tool before the GnRHa-stimulation test.


2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Farzaneh Shakouri ◽  
Linda Iorizzo ◽  
Hellen Mc Kinnon Edwards ◽  
Christina Anne Vinter ◽  
Karl Kristensen ◽  
...  

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