W L Clarke

Summary

Affiliation: University of Virginia
Country: USA

Publications

  1. ncbi The original Clarke Error Grid Analysis (EGA)
    William L Clarke
    University of Virginia, Children's Medical Center, Charlottesville, Virginia 22908, USA
    Diabetes Technol Ther 7:776-9. 2005
  2. ncbi Clinical requirements for closed-loop control systems
    William L Clarke
    Division of Pediatric Endocrinology, Department of Pediatrics, University of Virginia Health Sciences Center, Charlottesville, Virginia 22908, USA
    J Diabetes Sci Technol 6:444-52. 2012
  3. ncbi Closed-loop artificial pancreas using subcutaneous glucose sensing and insulin delivery and a model predictive control algorithm: the Virginia experience
    William L Clarke
    Division of Pediatric Endocrinology, Department of Pediatrics, University of Virginia Health Sciences Center, Charlottesville, Virginia 22908, USA
    J Diabetes Sci Technol 3:1031-8. 2009
  4. ncbi Statistical tools to analyze continuous glucose monitor data
    William Clarke
    Division of Pediatric Endocrinology, Department of Pediatrics, and Section on Computational Neuroscience, University of Virginia Health Sciences Center, Charlottesville, Virginia 22908, USA
    Diabetes Technol Ther 11:S45-54. 2009
  5. ncbi Evaluating clinical accuracy of continuous glucose monitoring systems: Continuous Glucose-Error Grid Analysis (CG-EGA)
    William L Clarke
    Department of Pediatrics, Box 800386, University of Virginia, Charlottesville, VA 22908, USA
    Curr Diabetes Rev 4:193-9. 2008
  6. ncbi Assessment and management of hypoglycemia in children and adolescents with diabetes
    William Clarke
    Department of Pediatrics, University of Virginia, Charlottesville, VA 22908, USA
    Pediatr Diabetes 9:165-74. 2008
  7. ncbi The artificial pancreas: how close are we to closing the loop?
    William L Clarke
    Division of Pediatric Endocrinology, Box 800386, University of Virginia School of Medicine, Charlottesville, Virginia 22908, USA
    Pediatr Endocrinol Rev 4:314-6. 2007
  8. ncbi Biopsychobehavioral model of risk of severe hypoglycemia. Self-management behaviors
    W L Clarke
    Department of Pediatrics, University of Virginia Health Sciences Center, Charlottesville 22908, USA
    Diabetes Care 22:580-4. 1999
  9. ncbi Evaluating the clinical accuracy of two continuous glucose sensors using continuous glucose-error grid analysis
    William L Clarke
    Division of Pediatric Endocrinology, Department of Pediatrics, University of Virginia Health System, Box 800386, Charlottesville, VA 22908, USA
    Diabetes Care 28:2412-7. 2005
  10. ncbi Hypoglycemia and the decision to drive a motor vehicle by persons with diabetes
    W L Clarke
    Department of Pediatrics, The University of Virginia Health Sciences Center, Charlottesville 22908, USA
    JAMA 282:750-4. 1999

Detail Information

Publications25

  1. ncbi The original Clarke Error Grid Analysis (EGA)
    William L Clarke
    University of Virginia, Children's Medical Center, Charlottesville, Virginia 22908, USA
    Diabetes Technol Ther 7:776-9. 2005
  2. ncbi Clinical requirements for closed-loop control systems
    William L Clarke
    Division of Pediatric Endocrinology, Department of Pediatrics, University of Virginia Health Sciences Center, Charlottesville, Virginia 22908, USA
    J Diabetes Sci Technol 6:444-52. 2012
    ..Instructions about these actions will constitute a major part of the education process of the patients before using CL systems and contribute to the manageability of these systems...
  3. ncbi Closed-loop artificial pancreas using subcutaneous glucose sensing and insulin delivery and a model predictive control algorithm: the Virginia experience
    William L Clarke
    Division of Pediatric Endocrinology, Department of Pediatrics, University of Virginia Health Sciences Center, Charlottesville, Virginia 22908, USA
    J Diabetes Sci Technol 3:1031-8. 2009
    ..The use of a new personalized model predictive control (MPC) algorithm to determine insulin doses to achieve and maintain BG levels between 70 and 140 mg/dl overnight and to control postprandial BG levels is presented...
  4. ncbi Statistical tools to analyze continuous glucose monitor data
    William Clarke
    Division of Pediatric Endocrinology, Department of Pediatrics, and Section on Computational Neuroscience, University of Virginia Health Sciences Center, Charlottesville, Virginia 22908, USA
    Diabetes Technol Ther 11:S45-54. 2009
    ..These methods should facilitate the extraction of information from, and the interpretation of, complex and voluminous CGM time series...
  5. ncbi Evaluating clinical accuracy of continuous glucose monitoring systems: Continuous Glucose-Error Grid Analysis (CG-EGA)
    William L Clarke
    Department of Pediatrics, Box 800386, University of Virginia, Charlottesville, VA 22908, USA
    Curr Diabetes Rev 4:193-9. 2008
    ..Information is presented on how to obtain assistance with the use of CG-EGA...
  6. ncbi Assessment and management of hypoglycemia in children and adolescents with diabetes
    William Clarke
    Department of Pediatrics, University of Virginia, Charlottesville, VA 22908, USA
    Pediatr Diabetes 9:165-74. 2008
  7. ncbi The artificial pancreas: how close are we to closing the loop?
    William L Clarke
    Division of Pediatric Endocrinology, Box 800386, University of Virginia School of Medicine, Charlottesville, Virginia 22908, USA
    Pediatr Endocrinol Rev 4:314-6. 2007
  8. ncbi Biopsychobehavioral model of risk of severe hypoglycemia. Self-management behaviors
    W L Clarke
    Department of Pediatrics, University of Virginia Health Sciences Center, Charlottesville 22908, USA
    Diabetes Care 22:580-4. 1999
    ..To identify self-management antecedents of low blood glucose (BG) (< 3.9 mmol/l) that might be easily recognized, treated, or avoided altogether...
  9. ncbi Evaluating the clinical accuracy of two continuous glucose sensors using continuous glucose-error grid analysis
    William L Clarke
    Division of Pediatric Endocrinology, Department of Pediatrics, University of Virginia Health System, Box 800386, Charlottesville, VA 22908, USA
    Diabetes Care 28:2412-7. 2005
    ..To compare the clinical accuracy of two different continuous glucose sensors (CGS) during euglycemia and hypoglycemia using continuous glucose-error grid analysis (CG-EGA)...
  10. ncbi Hypoglycemia and the decision to drive a motor vehicle by persons with diabetes
    W L Clarke
    Department of Pediatrics, The University of Virginia Health Sciences Center, Charlottesville 22908, USA
    JAMA 282:750-4. 1999
    ..6 and 3.6 mmol/L (47-65 mg/dL). However, to our knowledge, no data exist examining subjects' decisions to drive at various BG levels during their daily routine...
  11. ncbi Self-treatment of hypoglycemia while driving
    D J Cox
    University of Virginia Health System, Behavioral Medicine Center, Box 800 223, Charlottesville, VA 22908, USA
    Diabetes Res Clin Pract 54:17-26. 2001
    ..Drivers with Type 1 Diabetes Mellitus (T1DM) who did and did not engage in self-treatment during experimental hypoglycemia driving are compared physiologically and psychologically...
  12. ncbi Biopsychobehavioral model of severe hypoglycemia. II. Understanding the risk of severe hypoglycemia
    D J Cox
    University of Virginia Health Sciences Center, Behavioral Medicine Center, Charlottesville 22908, USA
    Diabetes Care 22:2018-25. 1999
    ..To evaluate the clinical/research utility of the biopsycho-behavioral model of severe hypoglycemia in differentiating patients with and without a history of severe hypoglycemia and in predicting occurrence of future severe hypoglycemia...
  13. ncbi Progressive hypoglycemia's impact on driving simulation performance. Occurrence, awareness and correction
    D J Cox
    Behavioral Medicine Center, University of Virginia Health System, Charlottesville 22908, USA
    Diabetes Care 23:163-70. 2000
    ..This study evaluated the blood glucose (BG) levels at which driving was impaired, impairment was detected, and corrective action was taken by subjects, along with the mechanisms underlying these three issues...
  14. ncbi Type 1 diabetic drivers with and without a history of recurrent hypoglycemia-related driving mishaps: physiological and performance differences during euglycemia and the induction of hypoglycemia
    Daniel J Cox
    Department of Psychiatry and Neurobehavioral Sciences, University of Virginia Health SciencesCenter, Charlottesville, Virginia, USA
    Diabetes Care 33:2430-5. 2010
    ..This increased risk appears to be attributable to a subgroup of drivers with type 1 diabetes. The hypothesis tested is that this vulnerable subgroup is more at risk for hypoglycemia and its disruptive effects on driving...
  15. ncbi Cognitive function is disrupted by both hypo- and hyperglycemia in school-aged children with type 1 diabetes: a field study
    Linda A Gonder-Frederick
    Behavioral Medicine Center, Department of Psychiatry and Neurobehavioral Sciences, University of Virginia Health Sciences Center, Charlottesville, Virgina, USA Linda Gonder Frederick
    Diabetes Care 32:1001-6. 2009
    ....
  16. ncbi Relationships between hyperglycemia and cognitive performance among adults with type 1 and type 2 diabetes
    Daniel J Cox
    Center for Behavioral Medicine Research, University of Virginia Health System, Charlottesville, Virginia 22908, USA
    Diabetes Care 28:71-7. 2005
    ..This study prospectively and objectively assessed the effects of hyperglycemia on cognitive-motor functioning in subjects' natural environment...
  17. ncbi Methods for quantifying self-monitoring blood glucose profiles exemplified by an examination of blood glucose patterns in patients with type 1 and type 2 diabetes
    Boris P Kovatchev
    University of Virginia Health System, Charlottesville, Virginia 22908, USA
    Diabetes Technol Ther 4:295-303. 2002
    ..0001). SMBG data allow for computing and frequent updating of various idiosyncratic diabetes characteristics and risk factors. The use of such computations may assist in optimizing patients' glycemic control...
  18. ncbi Safe at school: a Virginia experience
    Martha A Hellems
    Division of General Pediatrics, Department of Pediatrics, University of Virginia, Charlottesville, Virginia 22908, USA
    Diabetes Care 30:1396-8. 2007
    ....
  19. ncbi Predictors of fear of hypoglycemia in adolescents with type 1 diabetes and their parents
    Linda A Gonder-Frederick
    Department of Psychiatric Medicine, University of Virginia Health System, Charlottesville, VA 22908, USA
    Pediatr Diabetes 7:215-22. 2006
    ....
  20. ncbi The metabolic demands of driving for drivers with type 1 diabetes mellitus
    Daniel J Cox
    University of Virginia Health System, Charlottesville, VA 22908, USA
    Diabetes Metab Res Rev 18:381-5. 2002
    ..Conversely, symptoms caused by the stress of driving may be confused with hypoglycemia and lead to false alarms. This study examined the metabolic demand and the physiological stress of driving on type 1 diabetes mellitus (T1DM) drivers...
  21. ncbi Algorithmic evaluation of metabolic control and risk of severe hypoglycemia in type 1 and type 2 diabetes using self-monitoring blood glucose data
    Boris P Kovatchev
    University of Virginia Health System, Charlottesville, Virginia 22908, USA
    Diabetes Technol Ther 5:817-28. 2003
    ..SMBG data allow for accurate estimation of the two most important markers of metabolic control in T1DM and T2DM - HbA(1c) and risk for hypoglycemia...
  22. ncbi Evaluating the accuracy of continuous glucose-monitoring sensors: continuous glucose-error grid analysis illustrated by TheraSense Freestyle Navigator data
    Boris P Kovatchev
    Department of Psychiatric Medicine, University of Virginia Health System, Charlottesville, Virginia 22908, USA
    Diabetes Care 27:1922-8. 2004
    ....
  23. ncbi Quantifying temporal glucose variability in diabetes via continuous glucose monitoring: mathematical methods and clinical application
    Boris P Kovatchev
    University of Virginia Health System, Charlottesville, Virginia 22908, USA
    Diabetes Technol Ther 7:849-62. 2005
    ..As a result, important information about the temporal structure of the data is lost during the translation of raw sensor readings into clinically interpretable statistics and images...
  24. ncbi A critical appraisal of the continuous glucose-error grid analysis: response to Wentholt et al
    William L Clarke
    Diabetes Care 30:449-50; author reply 450-1. 2007
  25. ncbi Clinical application of emerging sensor technologies in diabetes management: consensus guidelines for continuous glucose monitoring (CGM)
    Irl B Hirsch
    Department of Medicine, University of Washington Medical Center Roosevelt, Seattle, Washington 98105, USA
    Diabetes Technol Ther 10:232-44; quiz 245-6. 2008
    ..Finally, researchers, manufacturers, payers, and advocacy groups must join forces on the policy level to create an environment conducive to managing continuous data, measuring outcomes, and formalizing best practices...