A DSM-5-Based Online Mental Health Referral Inventory: A Large-Scale Validation Study

2017 ◽  
Vol 35 (3) ◽  
pp. 231-246
Author(s):  
Robert Epstein ◽  
Megan Ho ◽  
Seojin Hyun ◽  
Christopher Le ◽  
Ronald E. Robertson ◽  
...  
Keyword(s):  
Mental Health ◽  
Validation Study ◽  
Large Scale ◽  
Scale Validation ◽  
Dsm 5 ◽  
Mental Health Referral

scholarly journals Large-scale validation of SCIAMACHY reflectance in the ultraviolet

2005 ◽  
Vol 5 (2) ◽  
pp. 1771-1796 ◽  
Author(s):  
G. van Soest ◽  
L. G. Tilstra ◽  
P. Stammes
Keyword(s):  
Radiative Transfer ◽  
Validation Study ◽  
Large Scale ◽  
Scale Validation ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Time Level ◽  
Standard Calibration ◽  
Level 1 ◽  
Reflectance Data

Abstract. In this paper we present an extensive validation of calibrated SCIAMACHY nadir reflectance in the UV (240–400 nm) by comparison with spectra calculated with a fast radiative transfer model. We use operationally delivered near-real-time level 1 data, processed with 5 standard calibration tools. A total of 9 months of data has been analysed. This is the first reflectance validation study incorporating such a large amount of data. It is shown that this method is a valuable tool for spotting spatial and temporal anomalies. We conclude that SCIAMACHY reflectance data in this wavelength range are stable over the investigated period. In addition, we show an example of an 10 anomaly in the data due to an error in the processing chain that could be detected by our comparison. This validation method could be extremely useful too for validation of other satellite spectrometers, such as OMI and GOME-2.

scholarly journals A large-scale internal validation study of unsupervised virtual trichrome staining technologies on nonalcoholic steatohepatitis liver biopsies

2020 ◽  
Author(s):  
Joshua J. Levy ◽  
Nasim Azizgolshani ◽  
Michael J. Andersen ◽  
Arief Suriawinata ◽  
Xiaoying Liu ◽  
...  
Keyword(s):  
Liver Disease ◽  
Validation Study ◽  
Large Scale ◽  
Scale Validation ◽  
Generative Adversarial Networks ◽  
Internal Validation ◽  
Adversarial Networks ◽  
Clinical Biomarkers ◽  
Diagnostic Decision ◽  
End Stage

AbstractNon-alcoholic steatohepatitis (NASH) is a fatty liver disease characterized by accumulation of fat in hepatocytes with concurrent inflammation and is associated with morbidity, cirrhosis and liver failure. After extraction of a liver core biopsy, tissue sections are stained with hematoxylin and eosin (H&E) to grade NASH activity, and stained with trichrome to stage fibrosis. Methods to computationally transform one stain into another on digital whole slide images (WSI) can lessen the need for additional physical staining besides H&E, reducing personnel, equipment, and time costs. Generative adversarial networks (GAN) have shown promise for virtual staining of tissue. We conducted a large-scale validation study of the viability of GANs for H&E to trichrome conversion on WSI (n = 574). Pathologists were largely unable to distinguish real images from virtual/synthetic images given a set of twelve Turing Tests. We report high correlation between staging of real and virtual stains ($${\rho} = 0.86$$ ρ = 0.86 ; 95% CI: 0.84–0.88). Stages assigned to both virtual and real stains correlated similarly with a number of clinical biomarkers and progression to End Stage Liver Disease (Hazard Ratio HR = 2.06, 95% CI: 1.36–3.12, p < 0.001 for real stains; HR = 2.02, 95% CI: 1.40–2.92, p < 0.001 for virtual stains). Our results demonstrate that virtual trichrome technologies may offer a software solution that can be employed in the clinical setting as a diagnostic decision aid.

scholarly journals A Large-Scale Internal Validation Study of Unsupervised Virtual Trichrome Staining Technologies on Non-alcoholic Steatohepatitis Liver Biopsies

2020 ◽  
Author(s):  
Joshua J. Levy ◽  
Nasim Azizgolshani ◽  
Michael J. Andersen ◽  
Arief Suriawinata ◽  
Xiaoying Liu ◽  
...  
Keyword(s):  
Liver Disease ◽  
Validation Study ◽  
Large Scale ◽  
Scale Validation ◽  
Generative Adversarial Networks ◽  
Internal Validation ◽  
Alcoholic Steatohepatitis ◽  
Adversarial Networks ◽  
Clinical Biomarkers ◽  
End Stage

AbstractNon-alcoholic steatohepatitis (NASH) is a fatty liver disease characterized by accumulation of fat in hepatocytes with concurrent inflammation and is associated with morbidity, cirrhosis and liver failure. After extraction of a liver core biopsy, tissue sections are stained with hematoxylin and eosin (H&E) to grade NASH activity, and stained with trichrome to stage fibrosis. Methods to computationally transform one stain into another on digital whole slide images (WSI) can lessen the need for additional physical staining besides H&E, reducing personnel, equipment, and time costs. Generative adversarial networks (GAN) have shown promise for virtual staining of tissue. We conducted a large-scale validation study of the viability of GANs for H&E to trichrome conversion on WSI (n=574). Pathologists were largely unable to distinguish real images from virtual/synthetic images given a set of twelve Turing Tests. We report high correlation between staging of real and virtual stains (ρ = 0.86; 95% CI: 0.84-0.88). Stages assigned to both virtual and real stains correlated similarly with a number of clinical biomarkers and progression to End Stage Liver Disease (Hazard Ratio HR = 2.06, CI 95% 1.36-3.12, P < 0.001 for real stains; HR = 2.02, CI 95% 1.40-2.92, p < 0.001 for virtual stains). Our results demonstrate that virtual trichrome technologies may offer a software solution that can be employed in the clinical setting as a diagnostic decision aid.

A large-scale validation study of the Medication Adherence Rating Scale (MARS)

2008 ◽  
Vol 100 (1-3) ◽  
pp. 53-59 ◽  
Author(s):  
Laura Fialko ◽  
Philippa A. Garety ◽  
Elizabeth Kuipers ◽  
Graham Dunn ◽  
Paul E. Bebbington ◽  
...  
Keyword(s):  
Medication Adherence ◽  
Validation Study ◽  
Large Scale ◽  
Rating Scale ◽  
Scale Validation

scholarly journals Large-scale validation of SCIAMACHY reflectance in the ultraviolet

2005 ◽  
Vol 5 (8) ◽  
pp. 2171-2180 ◽  
Author(s):  
G. van Soest ◽  
L. G. Tilstra ◽  
P. Stammes
Keyword(s):  
Radiative Transfer ◽  
Validation Study ◽  
Large Scale ◽  
Scale Validation ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Time Level ◽  
Standard Calibration ◽  
Level 1 ◽  
Reflectance Data

Abstract. In this paper we present an extensive validation of calibrated SCIAMACHY nadir reflectance in the UV (240-400 nm) by comparison with spectra calculated with a fast radiative transfer model. We use operationally delivered near-real-time level 1 data, processed with standard calibration tools. A total of 9 months of data has been analysed. This is the first reflectance validation study incorporating such a large amount of data. It is shown that this method is a valuable tool for spotting spatial and temporal anomalies. We conclude that SCIAMACHY reflectance data in this wavelength range are stable over the investigated period. In addition, we show an example of an anomaly in the data due to an error in the processing chain that could be detected by our comparison. This validation method could be extremely useful too for validation of other satellite spectrometers, such as OMI and GOME-2.

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