Treatment Trials

5 Clinical Trials for Various Conditions

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COMPLETED
Effect of Incidental Findings Information on Lung Cancer Screening Intent
Description

This online randomized controlled trial seeks to determine if inclusion of incidental findings information in a lung cancer screening decision aid affects screening intent among screening-eligible individuals. Participants will view either a decision aid with incidental findings information or without and indicate their intent to pursue lung cancer screening. Hypothesis: Inclusion of incidental findings in a decision aid will decrease intent to screen among participants.

COMPLETED
Understanding Genetic Incidental Findings in Your Family (UNIFY Study)
Description

Currently, there is no clear legal or ethical guidance about how researchers and IRBs ought to proceed when the research participant in a biobank is deceased and there is clinically relevant information that could be disclosed to family members. This study is designed to test a procedure offering genetic information to family members of research participants who participated in a pancreatic cancer biobank in a Health Insurance Portability and Accountability Act (HIPAA) -compliant design.

COMPLETED
IFACT: Incidental Findings in Advanced Cancer Therapy
Description

The proposed study, IFACT - Incidental Findings in Advanced Cancer Therapy, will address this research gap by examining MSK patients' attitudes, preferences, and information needs regarding incidental findings arising from tumor genomic profiling.

COMPLETED
Brief Title: Project 4 Example: Mixed Hybrid Checklist
Description

In "Mixed Hybrid Search", participants look for 3 specific target (e.g. this boot, this cat, this hat) and 3 categorical items (ANY fruit, ANY car, ANY game). In this task, participants tend to miss many categorical items. This is analogous to radiologists missing "incidental findings" when reading medical images. In this experiment, participants were given a checklist to help them to find categorical targets.

NOT_YET_RECRUITING
Improving Diagnostic Safety Through STeatosis Identification, Risk Stratification, and Referral in the ED
Description

Hepatic steatosis is a common radiographic "incidental finding" that is overlooked and underreported to patients. The investigators developed a clinical decision support system using machine learning and natural language processing that will prompt reporting to patients and provide ED clinicians risk stratified follow-up care recommendations. Data on both the implementation and effectiveness of our intervention resulting from this trial will inform future use with a goal of ultimately improving diagnostic safety and outcomes for patients with hepatic steatosis.