PREsurgical Cognitive Evaluation Via Digital clockfacEdrawing
NCT ID: NCT03175302
Last Updated: 2025-07-31
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
25240 participants
OBSERVATIONAL
2018-06-28
2027-05-31
Brief Summary
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Detailed Description
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Conditions
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Study Design
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COHORT
OTHER
Study Groups
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Surgical group
Baseline preoperative digital cognitive testing performance in adults to predict frequency and severity of clinician reported outcomes within the first three months post-surgery.
digital cognitive testing
The digital testing is hypothesized to identify latent features for differentiating cognitively impaired presurgical patient subgroups
Control
Non-surgery matched peers with the same testing.
digital cognitive testing
The digital testing is hypothesized to identify latent features for differentiating cognitively impaired presurgical patient subgroups
Interventions
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digital cognitive testing
The digital testing is hypothesized to identify latent features for differentiating cognitively impaired presurgical patient subgroups
Eligibility Criteria
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Inclusion Criteria
* screening within the University of Florida (UF) Health Preoperative clinic
* presurgical cognitive screening with the digital Clock Drawing Tool (dCDT)
Exclusion Criteria
* did not complete screening within the UF Health Preoperative clinic
* did not complete the presurgical cognitive screening with the digital Clock Drawing Tool (dCDT)
65 Years
ALL
No
Sponsors
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National Institute on Aging (NIA)
NIH
National Center for Advancing Translational Sciences (NCATS)
NIH
University of Florida
OTHER
Responsible Party
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Principal Investigators
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Catherine Price, Ph.D.
Role: PRINCIPAL_INVESTIGATOR
University of Florida
Patrick Tighe, MD, MS
Role: PRINCIPAL_INVESTIGATOR
University of Florida
Locations
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UF Health
Gainesville, Florida, United States
Countries
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Central Contacts
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Facility Contacts
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References
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Bandyopadhyay S, Wittmayer J, Libon DJ, Tighe P, Price C, Rashidi P. Explainable semi-supervised deep learning shows that dementia is associated with small, avocado-shaped clocks with irregularly placed hands. Sci Rep. 2023 May 6;13(1):7384. doi: 10.1038/s41598-023-34518-9.
Bandyopadhyay S, Dion C, Libon DJ, Price C, Tighe P, Rashidi P. Variational autoencoder provides proof of concept that compressing CDT to extremely low-dimensional space retains its ability of distinguishing dementia. Sci Rep. 2022 May 14;12(1):7992. doi: 10.1038/s41598-022-12024-8.
Other Identifiers
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OCR18881
Identifier Type: OTHER
Identifier Source: secondary_id
IRB201700747-N
Identifier Type: -
Identifier Source: org_study_id
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