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Trial registered on ANZCTR
Registration number
ACTRN12624001085561p
Ethics application status
Submitted, not yet approved
Date submitted
6/08/2024
Date registered
9/09/2024
Date last updated
9/09/2024
Date data sharing statement initially provided
9/09/2024
Type of registration
Prospectively registered
Titles & IDs
Public title
Pretrained Resnet for surgical Outcome prediction using PET Hypometabolism and Excised Tissue (PROPHET)
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Scientific title
Pretrained Resnet for surgical Outcome prediction using PET Hypometabolism and Excised Tissue (PROPHET) in participants planned for resective epilepsy surgery: Prospective Validation Study
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Secondary ID [1]
312694
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None
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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:
Drug resistant epilepsy
334696
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Condition category
Condition code
Neurological
331263
331263
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0
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Epilepsy
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Intervention/exposure
Study type
Interventional
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Description of intervention(s) / exposure
This is a prospective cohort study of patients who are undergoing resective epilepsy surgery for drug resistant epilepsy, designed to prospectively validate the clinical tool PROPHET, which predicts outcome following epilepsy surgery. PROPHET is a deep-learning based tool that utilises the preoperative T1-weighted MRI, preoperative 18F-FDG-PET scan, and a mask of the resection region as inputs to develop a predicted classification of the outcome following epilepsy surgery (Engel 1 vs Engel 2-4). PROPHET was developed by our team using retrospective data, and in model development, the mask of the resection region was generated from the difference map between the preoperative and postoperative T1-weighted MRIs, and therefore represented the actual resection region. However, to be clinically useful prior to epilepsy surgery, this tool will use a mask of the intended resection region in place of the actual resection region.
For all participants in this study, the intended resection region will be annotated manually prior to epilepsy surgery using brain imaging software such as ITK-SNAP or BrainLab. A prediction of the surgical outcome will be subsequently generated using the annotated intended resection region, preoperative MRI and preoperative PET (Model A). The predicted outcome generated by PROPHET will not be re-identified with study participants. Therefore, it will not be disclosed to the treating epileptologist or neurosurgeon prior to, or following, epilepsy surgery. The predicted outcome generated by PROPHET will not impact the clinical decision making about whether the patient will proceed to epilepsy surgery or the nature operation to be performed. The PROPHET predicted outcome will also not be shared with the participant.
We will subsequently observe the actual surgical outcome following epilepsy surgery up to 12 months, as measured by the Engel Surgical Outcome Scale. We will also collect the postoperative MRI in the subset of subjects for whom a postoperative MRI was acquired for clinical purposes.
The actual resection region will be generated for all patients who have a postoperative MRI, by generating a difference map between the preoperative and postoperative MRI (using the same method as in PROPHET development). Model B will be a repeat prediction using the preoperative MRI, preoperative PET and actual resection region.
For both models (model A using the intended resection region, and model B the actual resection region), the predicted Engel outcome and the actual Engel outcomes will be compared using area under the receiver operator characteristic (AUC) to validate the model.
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Intervention code [1]
329223
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Diagnosis / Prognosis
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Comparator / control treatment
Model B, which uses the actual resection region will be the comparator group. Model B uses inputs (preop MRI, preop FDG-PET and actual resection region) that were used in model development and internal/ external validation.
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Control group
Active
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Outcomes
Primary outcome [1]
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PROPHET validity
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Assessment method [1]
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Agreement between PROPHET's predicted outcome using the intended resection region (model A) and the actual outcome using the area under the receiver operating characteristic
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Timepoint [1]
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12 months postoperatively
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Secondary outcome [1]
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PROPHET validity
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Assessment method [1]
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Agreement between PROPHET's predicted outcome using the actual resection region (model B) and the actual outcome, using area under the receiver operating characteristic
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Timepoint [1]
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12 months postoperatively
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Secondary outcome [2]
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Similarity between the intended resection region and the actual resection region
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Assessment method [2]
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Similarity will be assessed using Dice Similarity Coefficient.
The intended resection region will be manually annotated preoperatively using brain imaging software such as BrainLab or ITK-SNAP.
The actual resection region will be generated using an automated segmentation algorithm called Epic-CHOP (https://github.com/iBrain-Lab/EPIC-CHOP) with possible manual adjustment following the quality control check.
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Timepoint [2]
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12 months postoperatively
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Secondary outcome [3]
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Change in quality of life
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Assessment method [3]
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Quality of Life in Epilepsy (QOLIE31) scores
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Timepoint [3]
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Measured at 0 months and 12 months postoperatively
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Secondary outcome [4]
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Change in depression
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Assessment method [4]
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Neurological Disorders Depression Inventory in Epilepsy (NDDIE) score
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Timepoint [4]
438318
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Measured at 0 months and 12 months postoperatively
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Secondary outcome [5]
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Change in anxiety
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Assessment method [5]
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Brief Epilepsy Anxiety Survey Instrument (BrEASI) score
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Timepoint [5]
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Measured at 0 months and 12 months postoperatively
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Eligibility
Key inclusion criteria
1. The participant can provide informed consent on their own behalf.
2. The participant is planned for resective epilepsy surgery (either temporal or extratemporal surgery).
3. A preoperative volumetric T1-weighted brain MRI is available.
4. A preoperative brain 18F-FDG-PET scan is available.
5. The participant is fluent in English.
6. The participant is eligible for Medicare
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Minimum age
18
Years
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Maximum age
No limit
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Sex
Both males and females
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Can healthy volunteers participate?
No
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Key exclusion criteria
1. Age less than or equal to 17 years old
2. Prior resective epilepsy surgery
3. Non-resective epilepsy surgeries, such as laser interstitial thermal therapy (LITT), vagus nerve stimulator (VNS) implantation, deep brain stimulator (DBS) implantation
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Study design
Purpose of the study
Diagnosis
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Allocation to intervention
Non-randomised trial
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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
The estimated sample size for this study is 50 participants. This number has been estimated to compare the expected model accuracy (area under the curve [AUC] = 0.8) to the null hypothesis (AUC = 0.5), in which the model does not perform better than random chance. In this calculation, power was set at 0.8, type 1 error at 0.05, and the expected proportion of positive cases (Engel class I) at 0.7. The estimated sample size will be re-evaluated in the interim feasibility analysis.
Obtaining a postoperative MRI is standard of care at our institution, at the 3 month mark. Therefore, we anticipate approximately 80% of the cohort (n=40) to be available for Model B.
For the analysis of model performance, two models will be assessed:
1. Model A: PROPHET prediction using the intended resection region
2. Model B: PROPHET prediction using the actual resection region
Data for each model will be classified as:
• True positive: model prediction “seizure free”; ground truth “seizure free”
• True negative: model prediction “not seizure free”; ground truth “not seizure free”
• False positive: model prediction “seizure free”; ground truth “not seizure free”
• False negative: model prediction “not seizure free”; ground truth “seizure free”
Using these classifications, we will calculate the AUC, kappa agreement between model prediction and outcome, sensitivity, specificity, positive predictive value, and negative predictive value.
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Recruitment
Recruitment status
Not yet recruiting
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Date of first participant enrolment
Anticipated
1/10/2024
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Actual
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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
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Actual
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Sample size
Target
50
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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]
26904
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The Alfred - Melbourne
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Recruitment postcode(s) [1]
42966
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3004 - Melbourne
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Funding & Sponsors
Funding source category [1]
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University
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Name [1]
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In kind support, Monash University
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Address [1]
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Country [1]
317125
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Australia
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Primary sponsor type
Hospital
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Name
Alfred Hospital
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Address
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Country
Australia
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Secondary sponsor category [1]
319387
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None
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Name [1]
319387
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Address [1]
319387
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Country [1]
319387
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Ethics approval
Ethics application status
Submitted, not yet approved
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Ethics committee name [1]
315878
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Alfred Hospital Ethics Committee
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Ethics committee address [1]
315878
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https://www.alfredhealth.org.au/research/ethics-research-governance
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Ethics committee country [1]
315878
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Australia
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Date submitted for ethics approval [1]
315878
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31/07/2024
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Approval date [1]
315878
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Ethics approval number [1]
315878
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Summary
Brief summary
Epilepsy surgery is the best treatment option for people living with epilepsy that cannot be controlled with medication alone, also known as drug resistant epilepsy. In people living with drug resistant epilepsy, epilepsy surgery offers a better chance of stopping seizures than medications alone, however, not all patients who have epilepsy surgery become seizure free. Many research studies that have tried to understand why some patients do not become seizure free after epilepsy surgery have focused on population data rather than looking at patients on an individual level. We have developed a tool that uses machine learning, a form of artificial intelligence, to make a personalised prediction of the outcome following epilepsy surgery. In its development, this tool used the brain imaging (magnetic resonance imaging [MRI] and positron emission tomography [PET] scans), as well as the actual surgical area, of patients who have already had epilepsy surgery. The aim of this study is to assess the accuracy of the tool when using the intended/ planned surgery in place of the actual resection region.
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Trial website
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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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Prof Terence O'Brien
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Address
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School of Translational Medicine, Monash University, 99 Commercial Rd Melbourne VIC 3004
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Country
136070
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Australia
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Phone
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+61 3 9903 0555
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Fax
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Email
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[email protected]
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Contact person for public queries
Name
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Dr Merran Courtney
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Address
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School of Translational Medicine, Monash University, 99 Commercial Rd Melbourne VIC 3004
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Country
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Australia
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Phone
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+61 3 9903 0555
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Fax
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Email
136071
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[email protected]
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Contact person for scientific queries
Name
136072
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Dr Merran Courtney
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Address
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School of Translational Medicine, Monash University, 99 Commercial Rd Melbourne VIC 3004
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Country
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Australia
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Phone
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+61 3 9903 0555
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Fax
136072
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Email
136072
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[email protected]
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Data sharing statement
Will individual participant data (IPD) for this trial be available (including data dictionaries)?
No
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No/undecided IPD sharing reason/comment
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What supporting documents are/will be available?
No Supporting Document Provided
Results publications and other study-related documents
Documents added manually
No documents have been uploaded by study researchers.
Documents added automatically
No additional documents have been identified.
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