Actively Recruiting
Vomiting Prevention in Children With Cancer
Led by The Hospital for Sick Children · Updated on 2026-03-05
1332
Participants Needed
1
Research Sites
104 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
The goal of this single arm trial is to learn if a machine learning (ML) model predicting the risk of vomiting within the next 96 hours will impact vomiting outcomes in inpatient cancer pediatric patients. The main questions it aims to answer are whether an ML model predicting the risk of vomiting within the next 96 hours will: Primary 1\. Reduce the proportion with any vomiting within the 96-hour window Secondary 1. Reduce the number of vomiting episodes 2. Increase the proportion receiving care pathway-consistent care 3. Impact on number of administrations and costs of antiemetic medications Newly admitted participants will have a ML model predict the risk of vomiting within the next 96 hours according to their medical admission information. The prediction will be made at 8:30 AM following admission. Pharmacists will be charged with bringing information about patients' vomiting risk to the attention of the medical team and implementing interventions.
CONDITIONS
Official Title
Vomiting Prevention in Children With Cancer
Who Can Participate
Eligibility Criteria
You may qualify if you...
- All pediatric patients admitted to the oncology service at SickKids
You will not qualify if you...
- Pediatric patients admitted to the oncology service at SickKids that are discharged prior to prediction time
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Trial Site Locations
Total: 1 location
1
The Hospital for Sick Children
Toronto, Ontario, Canada, M5G1X8
Actively Recruiting
Research Team
L
Lillian Sung, MD, PhD
CONTACT
A
Agata Wolochacz, BMSc
CONTACT
How is the study designed?
Study Type
INTERVENTIONAL
Masking
NONE
Allocation
NA
Model
SINGLE_GROUP
Primary Purpose
SUPPORTIVE_CARE
Number of Arms
1
Not the Right Trial for You?
Explore thousands of other clinical trials that might be a better match.
Sign up to get personalized trial recommendations delivered to your inbox.
Already have an account? Log in here