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Trial details imported from ClinicalTrials.gov
For full trial details, please see the original record at
https://clinicaltrials.gov/study/NCT06647225
Registration number
NCT06647225
Ethics application status
Date submitted
14/10/2024
Date registered
17/10/2024
Date last updated
25/02/2025
Titles & IDs
Public title
Using Artificial Intelligence to Screen for Hip Dysplasia
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Scientific title
Artificial Intelligence Augmented Ultrasound for Developmental Dysplasia of the Hip: a Validity Study
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Secondary ID [1]
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107227
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Universal Trial Number (UTN)
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Trial acronym
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Linked study record
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Health condition
Health condition(s) or problem(s) studied:
Developmental Dysplasia of Hip
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Condition category
Condition code
Musculoskeletal
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Other muscular and skeletal disorders
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Intervention/exposure
Study type
Interventional
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Description of intervention(s) / exposure
Treatment: Devices - Artificial intelligence augmented ultrasound
Other: All active participants - All participants will undergo an AI-augmented ultrasound and there will be no active comparator
Treatment: Devices: Artificial intelligence augmented ultrasound
The hip ultrasound is performed using a handheld device (Exo Iris) that uses a pocket-sized ultrasound probe and is run through an application on an IoS (Apple mobile) operation system. . A real-time algorithm detects and records the anatomical landmarks. When there are enough images for analysis the operator is notified that the scan is complete. From here, the images are then classified as: the hips as "healthy", "follow-up recommended," or "suboptimal (repeat scan)".
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Intervention code [1]
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Treatment: Devices
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Comparator / control treatment
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Control group
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Outcomes
Primary outcome [1]
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Artificial Intelligence augmented ultrasound screening test capability: Sensitivity
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Assessment method [1]
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Artificial intelligence (AI) augmented ultrasound results will be compared to standard ultrasound imaging to calculate sensitivity (\[number of true positive cases detected/(number of true positive cases detected + number of false negative cases detected)\] X 100). Groups will be defined as follows: * True positive cases: Flagged for follow-up after the AI assessment of ultrasound sweeps from the portable probe and have a diagnosis of DDH from traditional ultrasound reports. * False positive cases: Flagged for follow-up after the AI assessment of the ultrasound sweeps and do not have a diagnosis of DDH from the traditional ultrasound reports * False negative cases: Return a "normal hips" assessment after the AI assessment of the ultrasound probe and have a diagnosis of DDH from traditional ultrasound reports * True negative cases: Return a "normal hips" assessment after the AI assessment of the ultrasound probe and do not have a diagnosis of DDH from traditional ultrasound reports
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Timepoint [1]
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1 day, both ultrasound scans will be performed on the same day
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Primary outcome [2]
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Artificial Intelligence augmented ultrasound screening test capability: Specificity
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Assessment method [2]
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Artificial intelligence (AI) augmented ultrasound results will be compared to standard ultrasound imaging to calculate specificity (\[number of true negative cases detected/(number of false positive cases detected + number of true negatives cases detected\] X 100). Groups will be defined as follows: * True positive cases: Flagged for follow-up after the AI assessment of ultrasound sweeps from the portable probe and have a diagnosis of DDH from traditional ultrasound reports. * False positive cases: Flagged for follow-up after the AI assessment of the ultrasound sweeps and do not have a diagnosis of DDH from the traditional ultrasound reports * False negative cases: Return a "normal hips" assessment after the AI assessment of the ultrasound probe and have a diagnosis of DDH from traditional ultrasound reports * True negative cases: Return a "normal hips" assessment after the AI assessment of the ultrasound probe and do not have a diagnosis of DDH from traditional ultrasound reports
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Timepoint [2]
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1 day, both ultrasound scans will be performed on the same day
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Primary outcome [3]
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Artificial Intelligence augmented ultrasound screening test capability: Positive predictive value (PPV)
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Assessment method [3]
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Artificial intelligence (AI) augmented ultrasound will be compared to standard ultrasound to calculate PPV (\[number of true positive cases detected/(number of true positive cases detected + number of false positive cases predicted) X 100). Groups will be defined as follows: * True positive cases: Flagged for follow-up after the AI assessment of ultrasound sweeps from the portable probe and have a diagnosis of DDH from traditional ultrasound reports. * False positive cases: Flagged for follow-up after the AI assessment of the ultrasound sweeps and do not have a diagnosis of DDH from the traditional ultrasound reports * False negative cases: Return a "normal hips" assessment after the AI assessment of the ultrasound probe and have a diagnosis of DDH from traditional ultrasound reports * True negative cases: Return a "normal hips" assessment after the AI assessment of the ultrasound probe and do not have a diagnosis of DDH from traditional ultrasound reports
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Timepoint [3]
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1 day, both ultrasound scans will be performed on the same day
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Primary outcome [4]
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Artificial Intelligence augmented ultrasound screening test capability: Negative predictive value (NPV)
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Assessment method [4]
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Artificial intelligence (AI) augmented ultrasound will be compared to standard ultrasound to calculate NPV \[number of true negative cases detected/(number of false negatives detected + number of true negative cases detected) X 100. Groups will be defined as follows: * True positive cases: Flagged for follow-up after the AI assessment of ultrasound sweeps from the portable probe and have a diagnosis of DDH from traditional ultrasound reports. * False positive cases: Flagged for follow-up after the AI assessment of the ultrasound sweeps and do not have a diagnosis of DDH from the traditional ultrasound reports * False negative cases: Return a "normal hips" assessment after the AI assessment of the ultrasound probe and have a diagnosis of DDH from traditional ultrasound reports * True negative cases: Return a "normal hips" assessment after the AI assessment of the ultrasound probe and do not have a diagnosis of DDH from traditional ultrasound reports
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Timepoint [4]
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1 day, both ultrasound scans will be performed on the same day
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Secondary outcome [1]
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Factors associated with differences in device sensitivity
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Assessment method [1]
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Sensitivity will be calculated between groups: 1. Degree of dysplasia as defined by the Graf classification 2. Sex 3. Infant age (categorised as 4-7.99 weeks, 8-11.99 weeks, 12-15.99 weeks, 16-20 weeks) 4. Number of scans performed by device operator (less than or greater than 60 scans).
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Timepoint [1]
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1 day, all data will be collected from day of scan
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Secondary outcome [2]
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Factors associated with differences in device specificity
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Assessment method [2]
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Analyses will stratified to look at differences in specificity between groups: 1. Degree of dysplasia as defined by the Graf classification 2. Sex 3. Infant age (categorised as 4-7.99 weeks, 8-11.99 weeks, 12-15.99 weeks, 16-20 weeks) 4. Number of scans performed by device operator (less than or greater than 60 scans).
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Timepoint [2]
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1 day, all data will be collected from day of scan
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Secondary outcome [3]
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Factors associated with differences in device positive predictive value
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Assessment method [3]
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Positive predictive value will be compared between groups: 1. Degree of dysplasia as defined by the Graf classification 2. Sex 3. Infant age (categorised as 4-7.99 weeks, 8-11.99 weeks, 12-15.99 weeks, 16-20 weeks) 4. Number of scans performed by device operator (less than or greater than 60 scans).
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Timepoint [3]
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1 day, all data will be collected from day of scan
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Secondary outcome [4]
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Factors associated with differences in device negative predictive value
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Assessment method [4]
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Negative predictive value will be compared between groups: 1. Degree of dysplasia as defined by the Graf classification 2. Sex 3. Infant age (categorised as 4-7.99 weeks, 8-11.99 weeks, 12-15.99 weeks, 16-20 weeks) 4. Number of scans performed by device operator (less than or greater than 60 scans).
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Timepoint [4]
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1 day, all data will be collected from day of scan
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Secondary outcome [5]
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Device operator reliability in performing successful scans
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Assessment method [5]
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Device operators' reliability will be recorded as percentage of scans performed that return a suboptimal result. This will be done by graphing number of scans performed by operators (operator experience) (x axis) against proportion of sub-optimal scans (y axis) to visually identify if a steady state is achieved.
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Timepoint [5]
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12 months or entire study duration
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Secondary outcome [6]
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Acquisition of successful scans
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Assessment method [6]
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The total proportion of infants unable to be scanned with the Artificial Intelligence augmented ultrasound device and reasons why scans were unsuccessful will from the entire sample. A higher frequency of successful scan acquisition will indicate better device performance.
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Timepoint [6]
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12 months or entire study duration
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Secondary outcome [7]
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Time taken to acquire scan
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Assessment method [7]
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Time to acquire the image will be calculated from the initiation of the scan to the time that the software indicates image acquisition is complete. The time to receive results will be calculated from the time of completion acquisition to the time the final recommendation is provided. A lower successful scan time will be indicative of higher feasibility.
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Timepoint [7]
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1 day, calculated at time of scan
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Secondary outcome [8]
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Caregiver perspectives on their infant undergoing the artificial intelligence augmented ultrasound
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Assessment method [8]
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Caregivers will be asked to answer a purpose-built survey that has been piloted in Canadian studies (3 questions rated from 0-10, where 10 indicates a more positive experience) in addition to Australian-specific closed and open-ended questions.
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Timepoint [8]
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1 day, caregivers will be asked to complete immediately following the scan
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Secondary outcome [9]
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Operator perspectives on performing the artificial intelligence augmented ultrasound
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Assessment method [9]
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Operators will be asked to complete the 10-item System Usability Questionnaire which measures the perceived ease of using technological devices. Scores are calculated on a 5-point Likert scale where 1=Strongly disagree and 5=Strongly agree. A single composite score out of 100 is calculated from all 10 items and indicates the overall useability of the device, where a higher score indicates better useability. In addition to this, two open-ended questions (Are there any further comments you would like to make about what you liked about the device?" and "are there any further comments you would like to make about what you didn't like about the device?") will be asked.
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Timepoint [9]
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At the conclusion of their involvement in the study device (up to 12 months)
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Eligibility
Key inclusion criteria
* Enrolled in the VicHip study
* Is 4-20 weeks of age at enrolment
* Is attending The Royal Children's Hospital for the purpose of the potential diagnosis of DDH
* Has a diagnostic (standard) hip ultrasound on the day of their out-patient appointment
* Has a legally acceptable representative capable of understanding the informed consent document and providing consent on the participant's behalf.
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Minimum age
4
Weeks
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Maximum age
20
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Sex
Both males and females
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Can healthy volunteers participate?
Yes
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Key exclusion criteria
Participants will be excluded from enrolment if:
• They are currently receiving treatment for DDH
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Study design
Purpose of the study
Other
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Allocation to intervention
Not applicable
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Procedure for enrolling a subject and allocating the treatment (allocation concealment procedures)
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Methods used to generate the sequence in which subjects will be randomised (sequence generation)
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Masking / blinding
Open (masking not used)
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Who is / are masked / blinded?
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Intervention assignment
Single group
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Other design features
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Phase
Not applicable
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Type of endpoint/s
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Statistical methods / analysis
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Recruitment
Recruitment status
Recruiting
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Data analysis
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Reason for early stopping/withdrawal
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Other reasons
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Date of first participant enrolment
Anticipated
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Actual
6/12/2024
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Date of last participant enrolment
Anticipated
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Actual
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Date of last data collection
Anticipated
1/11/2025
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Actual
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Sample size
Target
240
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Accrual to date
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Final
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Recruitment in Australia
Recruitment state(s)
VIC
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Recruitment hospital [1]
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Royal Children's Hospital - Parkville
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Recruitment postcode(s) [1]
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3052 - Parkville
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Funding & Sponsors
Primary sponsor type
Other
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Name
Murdoch Childrens Research Institute
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Address
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Country
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Ethics approval
Ethics application status
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Summary
Brief summary
The goal of this clinical trial is to learn if an ultrasound scan using artificial intelligence can accurately screen for hip dysplasia. Researchers will compare the artificial intelligence ultrasound results to the standard ultrasound measures to see if the artificial intelligence ultrasound scan can accurately screen for hip dysplasia. It will also seek to understand how parents feel about their children undergoing this scan. Participants will: * Have an additional ultrasound performed on their child at their scheduled outpatient's appointment for hip dysplasia * Complete a short questionnaire about the experience of having the measurement performed on their child
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Trial website
https://clinicaltrials.gov/study/NCT06647225
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Trial related presentations / publications
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Public notes
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Contacts
Principal investigator
Name
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Leo T Donnan
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Address
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Murdoch Childrens Research Institute
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Country
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Phone
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Fax
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Email
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Contact person for public queries
Name
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Natalie K Hyde, BBiomedSc (Hons), PhD
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Address
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Country
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Phone
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61 3 9936 6246
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Fax
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Email
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[email protected]
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Contact person for scientific queries
Data sharing statement
What supporting documents are/will be available?
No Supporting Document Provided
Results publications and other study-related documents
No documents have been uploaded by study researchers.
Results not provided in
https://clinicaltrials.gov/study/NCT06647225
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