Validation of an Online Knee Pain Map and Questionnaire: A Probabilistic Diagnostic Tool

This study has been terminated.
Information provided by (Responsible Party):
Brock Foster, University of California, Los Angeles Identifier:
First received: December 6, 2011
Last updated: March 26, 2012
Last verified: March 2012

December 6, 2011
March 26, 2012
December 2011
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The ability of the UCLA modeling software to predict diagnosis based on questionnaire answers [ Time Frame: One year ] [ Designated as safety issue: No ]
UCLA has developed modeling software that may be accurate at predicting diagnoses depending on the answers given by patients to an online questionnaire and knee pain drawing map. The accuracy of the software has not been tested or validated. This study will determine the accuracy of this software by comparing UCLA orthopedic surgeon input diagnosis to that output by the modeling software following completion of the questionnaire by study participants.
Same as current
Complete list of historical versions of study NCT01492244 on Archive Site
Accuracy of patient input diagnosis compared to orthopedic surgeon diagnosis [ Time Frame: One Year ] [ Designated as safety issue: No ]
Patients may inaccurately input "known diagnoses" into the online questionnaire because they were diagnosed inaccurately by their doctor, input the wrong diagnosis into the questionnaire by accident, or were never diagnosed with a condition but they input a diagnosis. Therefore, because the modeling software is contingent on accurate patient input diagnoses, determining if patients accurately input their diagnoses into the questionnaire by comparing surgeon input diagnosis to patient input diagnosis may be helpful in elucidating modeling inaccuracy.
Same as current
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Validation of an Online Knee Pain Map and Questionnaire: A Probabilistic Diagnostic Tool
Alidation of an Online Knee Pain Map and Questionnaire: A Probabilistic Diagnostic Tool
"Blank" has designed a medical diagnostic system in the form of an unvalidated online questionnaire and drawing tool used to describe and identify the location of knee pain, respectively. A component of the survey includes the patient inputting their diagnosis as the etiology of their knee pain. Dr. Ivo Dinov's team has used the data from 100,000 patient surveys to construct a probabilistic model to diagnose those who fill out the questionnaire and knee pain map but do not have a diagnosis. However, the validity of the online survey and the accuracy of the probabilistic model has not been confirmed in patients with known diagnoses. Therefore, the purpose of this study will be to recruit patients with knee pain at UCLA orthopedic clinics to complete the online survey which will then be applied to the probabilistic model to output possible diagnoses. The results will be compared to the actual diagnosis assigned to that patient in the clinic. If validated, the online survey may serve as a tool for diagnostic and research purposes.
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Observational Model: Cohort
Time Perspective: Prospective
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Non-Probability Sample
Patients older than 18 years old with knee pain that contains a known diagnosis for their pain.
Knee Pain
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Patients with knee pain and a known diagnosis
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*   Includes publications given by the data provider as well as publications identified by Identifier (NCT Number) in Medline.
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Inclusion Criteria:

  • patients with knee pain and a known diagnosis for their pain
  • patients older than 18 years old

Exclusion Criteria:

  • patients that are unable or unwilling to complete the online survey.
  • patients who do not have a diagnosis for their knee pain
18 Years and older
Contact information is only displayed when the study is recruiting subjects
United States
Brock Foster, University of California, Los Angeles
Brock Foster
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Not Provided
University of California, Los Angeles
March 2012

ICMJE     Data element required by the International Committee of Medical Journal Editors and the World Health Organization ICTRP