Identifying environmental reservoirs of Clostridium difficile with a scent detection dog: preliminary evaluation

2017 ◽  
Vol 97 (2) ◽  
pp. 140-145 ◽  
Author(s):  
E. Bryce ◽  
T. Zurberg ◽  
M. Zurberg ◽  
S. Shajari ◽  
D. Roscoe
Keyword(s):  
Clostridium Difficile ◽  
Preliminary Evaluation ◽  
Scent Detection ◽  
Detection Dog

An approach to identify bias in scent detection dog testing

2017 ◽  
Vol 189 ◽  
pp. 1-12 ◽  
Author(s):  
Dorothea Johnen ◽  
Wolfgang Heuwieser ◽  
Carola Fischer-Tenhagen
Keyword(s):  
Scent Detection ◽  
Detection Dog

scholarly journals A Need for Greater Inclusivity and Diversity in Scent Detection Dog Research: A Reply to Lazarowski et al. and Byosiere et al.

2019 ◽  
Vol 14 ◽  
pp. 87-89
Author(s):  
Camille A. Troisi ◽  
Daniel S. Mills ◽  
Anna Wilkinson ◽  
Helen E. Zulch
Keyword(s):  
Scent Detection ◽  
Detection Dog

A detection dog to identify patients with Clostridium difficile infection during a hospital outbreak

2014 ◽  
Vol 69 (5) ◽  
pp. 456-461 ◽  
Author(s):  
Marije K. Bomers ◽  
Michiel A. van Agtmael ◽  
Hotsche Luik ◽  
Christina MJE. Vandenbroucke-Grauls ◽  
Yvo M. Smulders
Keyword(s):  
Clostridium Difficile ◽  
Clostridium Difficile Infection ◽  
Hospital Outbreak ◽  
Detection Dog

scholarly journals Using Dog Scent Detection as a Point-of-Care Tool to Identify Toxigenic Clostridium difficile in Stool

2018 ◽  
Vol 5 (8) ◽  
Author(s):  
Maureen T Taylor ◽  
Janine McCready ◽  
George Broukhanski ◽  
Sakshi Kirpalaney ◽  
Haydon Lutz ◽  
...  
Keyword(s):  
Clostridium Difficile ◽  
Diagnostic Tool ◽  
Interrater Reliability ◽  
Point Of Care ◽  
Toxin Gene ◽  
Operating Characteristics ◽  
Widespread Application ◽  
Cohen's Kappa ◽  
Scent Detection ◽  
Stool Specimens

Abstract We evaluated the operating characteristics of 2 comparably trained dogs as a “point-of-care” diagnostic tool to detect toxin gene-positive Clostridium difficile. Although each dog could detect toxin gene-positive C difficile in stool specimens with sensitivities of 77.6 and 92.6 and specificities of 85.1 and 84.5, respectively, interrater reliability is only modest (Cohen’s kappa 0.52), limiting widespread application.

scholarly journals Handler beliefs affect scent detection dog outcomes

2011 ◽  
Vol 14 (3) ◽  
pp. 387-394 ◽  
Author(s):  
Lisa Lit ◽  
Julie B. Schweitzer ◽  
Anita M. Oberbauer
Keyword(s):  
Scent Detection ◽  
Detection Dog

scholarly journals Using scent detection dogs to identify environmental reservoirs of Clostridium difficile: Lessons from the field

2019 ◽  
pp. 93-95 ◽  
Author(s):  
Chen Li ◽  
Teresa Zurberg ◽  
Jaime Kinna ◽  
Kushal Acharya ◽  
Jack Warren ◽  
...  
Keyword(s):  
Clostridium Difficile ◽  
Mitigation Strategies ◽  
Scent Detection

Environmental reservoirs have been implicated in transmission of Clostridium difficile infections. Scent detection by canines has demonstrated promising ability to rapidly triage hospital surfaces and equipment. 18 months of data collected post-implementation of the canine scent detection project at Vancouver Coastal Health were used to identify key environmental reservoirs for C. difficile and possible mitigation strategies.

Preliminary Evaluation of a Weibull Function for Fitting Slow-Component Eye Velocity over the Time Course of Caloric-Induced Nystagmus

1989 ◽  
Vol 32 (3) ◽  
pp. 681-687 ◽  
Author(s):  
C. Formby ◽  
B. Albritton ◽  
I. M. Rivera
Keyword(s):  
Time Course ◽  
Slow Component ◽  
Weibull Function ◽  
Preliminary Evaluation ◽  
Mathematical Function ◽  
Before And After ◽  
Best Fitting ◽  
Eye Velocity ◽  
Fitting Parameters ◽  
Weibull Equation

We describe preliminary attempts to fit a mathematical function to the slow-component eye velocity (SCV) over the time course of caloric-induced nystagmus. Initially, we consider a Weibull equation with three parameters. These parameters are estimated by a least-squares procedure to fit digitized SCV data. We present examples of SCV data and fitted curves to show how adjustments in the parameters of the model affect the fitted curve. The best fitting parameters are presented for curves fit to 120 warm caloric responses. The fitting parameters and the efficacy of the fitted curves are compared before and after the SCV data were smoothed to reduce response variability. We also consider a more flexible four-parameter Weibull equation that, for 98% of the smoothed caloric responses, yields fits that describe the data more precisely than a line through the mean. Finally, we consider advantages and problems in fitting the Weibull function to caloric data.

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