Social MediumNCT05442762
Social Media-based Vaccine Confidence and Hesitancy Monitoring
An observational study of Social Medium and Data Collection, run by Fudan University.
Trial summary
History and scientific evidence show that it is critical to maintain public trust and confidence in vaccination. Any crisis in confidence has the potential to cause significant disruption and a detrimental impact on vaccination. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Social Medium, Data Collection, Patient Acceptance of Health Care
- Type of treatment
- Vaccines
- How many people
- 0 participants
- Where
- No sites listed yet
- Study dates
- 1 Mar 2022to 24 Jun 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: Vaccine-related posts on social media are the object of our research to assess public's vaccine confidence and hesitancy. Each post record comprises account name, profiles, contents, post time, the number of followers, and engagement data.
Who may be able to join
- Tweets and weico posts related to vaccines
- Published in 2015-2022
- English tweets
- Tweets/posts from personal accounts.
Who cannot join
- Tweets/posts from news, organization accounts, or authenticated users
- Non English tweets.
About this study
Official title: A Social Media-based Machine Learning Study to Monitor Vaccine Confidence and Hesitancy and Early Warn Emerging Vaccine-related Risks in Real Time
History and scientific evidence show that it is critical to maintain public trust and confidence in vaccination. Any crisis in confidence has the potential to cause significant disruption and a detrimental impact on vaccination. Vaccine hesitancy is a complex and context-specific issue that varies across time, place, and vaccines. It has been cited by World Health Organization(WHO) as one of the top ten threats to global health in 2019. Coronavirus disease(COVID-19) pandemic may change public confidence in vaccines. Therefore, it is necessary to establish a surveillance system to monitor vaccine confidence and hesitancy in real time.
To date, a growing body of literature has used social media platforms such as Twitter and weico for public health research. Large amounts of real time data posted on social media platforms can be used to quickly identify the public's attitudes on vaccines, as a way to support health communication and health promotion, messaging. However, textual data on social media is difficult to be analyzed. Recent progress in machine learning makes it possible to automatically analyze textual data on social media in real time.
In this study, the investigators will establish a social media surveillance and analysis platform on vaccines, develop a series of machine learning models to monitor vaccine confidence and early detect emerging vaccine-related risks, and assess public communication around vaccines. The investigators will assess the temporal and spatial distribution of vaccine confidence and hesitancy globally using Twitter data and in China using weico data, for all vaccines and Human Papilloma Virus(HPV) vaccine, respectively. Our study will guide the design of effective health communication strategies to improve vaccine confidence.
Read the detailed description
- Collect and update social media data regarding vaccines The investigators will automatically collect all social media posts regarding vaccines in real time. Social media cohort database will be established and updated for all vaccines and Human Papilloma Virus(HPV) vaccine, respectively.
- Monitor vaccine confidence and hesitancy in real time: deep (supervised) machine learning models Deep learning model, a supervised machine learning technique, will be used to analyze text data on social media in real time according to the predefined vaccine confidence and hesitancy framework. The investigators will first manually annotate a subset of social media posts (20,000 posts) regarding vaccines. The initial manually-annotated posts are then used to train and evaluate deep learning models. Deep learning models with the best performance are selected and applied to classify all vaccine-related posts according to the vaccine confidence and hesitancy framework.
- Monitor emerging concerns and sentiment swings in real time to early warn vaccine-related risks or crises: topic (unsupervised) machine learning models and linguistic analysis There are some topics outside of the predefined vaccine confidence and hesitancy framework used in deep learning models, and new topics emerge in any time. Vaccine crisis would influence public sentiments. Monitoring emerging topics and sentiment swings will provide early warning of vaccine-related risks or crises. Use Topic Modeling, an unsupervised machine learning technique that can automatically classify text to representative topics in social media, to monitor emerging topics and concerns regarding vaccines.
- Assess public engagement on social media to inform effective health communication strategies: social media engagement analysis Besides posts data on social media, engagement data of posts are also available to be analyzed, including likes, comments, and shares of posts. The investigators will conduct social media engagement analysis to investigate public communication around vaccines online. This will guide the design of effective health communication strategies.
- Establish social media surveillance and analysis platform for vaccine confidence and crisis Through the steps above, the investigators will establish a social media surveillance and analysis platform for vaccine confidence and crisis. Time-series trends, geographic variation, and associated factors of the indicators produced above will be presented to monitor vaccine confidence in real time, early warn emerging risks or crises, and inform effective health communication strategies.
- Past research experience The investigators have conducted a series of relevant studies to analyze social media data using machine learning techniques during the COVID-19 epidemic, covering COVID-19 vaccine confidence and public response to COVID-19. These experiences make the current study feasible.
- Conditions
- Social Medium, Data Collection, Patient Acceptance of Health Care, Immunization Programs, Vaccination Refusal, Information Dissemination
- Keywords
- vaccination hesitancy, vaccine confidence, social media, infodemiology, machine learning
- Medical subject headings
- Patient Acceptance of Health Care, Vaccination Refusal, Vaccination Hesitancy
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Global Database of Vaccine Related Posts | Tweets in English from Twitter and posts from weico from 2015 to 2022 for all vaccines. The investigators only included posts from individual accounts and excluded those from news, organizational accounts, or verified users. | |
| Global Database of HPV Vaccine Related Posts | Tweets in English from Twitter and posts from weico from 2015 to 2022 for HPV vaccine. The investigators only included posts from individual accounts and excluded those from news, organizational accounts, or verified users. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Changes in the prevalence of vaccine confidence and hesitancy
Vaccine confidence refers to the public's tweets expressing trust in the safety and effectiveness of vaccine, recognition of the vaccination necessity, and vaccine acceptance. Vaccine hesitancy means that the tweets express vaccine-related misinformation and rumors, worry about the safety and effectiveness of the vaccine, and vaccine rejection. The investigators will calculate the ratio of these two categories in all vaccine-related tweets as the prevalence of vaccine confidence and vaccine hesitancy.
Measured: Change from baseline prevalence of vaccine confidence and vaccine hesitancy at 1 year.
Secondary outcomes (2)
Changes in the prevalence of machine-generated topics
Machine-generated topics refer to vaccine-related topics automatically generated through machine learning methods, such as political conspiracy, vaccine exemption, vaccine adverse events, and others. The investigators will calculate the ratio of tweets involved in each machine-generated topic in all vaccine-related tweets as the prevalence of machine-generated topics.
Measured: Change from baseline prevalence of machine-generated topics at 1 year.
Changes in the public engagement on social media
Public engagement on social media is a comprehensive evaluation index to measure the transmit, reply, and like. The investigators will record the baseline and corresponding values after one year.
Measured: Change from baseline public engagement on social media at 1 year.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Jul 2022
- Study start1 Mar 2022
- Primary completion1 Jun 2022
- Study completion24 Jun 2022
Why it stopped: Adjustment of research plan
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (14)Papers the sponsor has linked to this study.
- Other trials for Social MediumStudies of the same condition, open and under way.
- More trials run by Fudan UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 0 participants (actual)
Sponsor and investigators
- Lead sponsor
- Fudan University
- Sponsor type
- University, hospital or other
- Collaborators
- Merck Sharp & Dohme LLC
- Responsible party
- Principal investigator, Zhiyuan Hou, Fudan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05442762
- Sponsor's protocol ID
- ECT2112016948
- Status last verified
- Jun 2022
- First submitted
- 21 Mar 2022
- Last updated
- 5 Jul 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: According to the terms of the agreement, individual data can not be shared.
Publications
MacDonald NE; SAGE Working Group on Vaccine Hesitancy. Vaccine hesitancy: Definition, scope and determinants. Vaccine. 2015 Aug 14;33(34):4161-4. doi: 10.1016/j.vaccine.2015.04.036. Epub 2015 Apr 17.
PubMed 25896383Larson HJ, Jarrett C, Eckersberger E, Smith DM, Paterson P. Understanding vaccine hesitancy around vaccines and vaccination from a global perspective: a systematic review of published literature, 2007-2012. Vaccine. 2014 Apr 17;32(19):2150-9. doi: 10.1016/j.vaccine.2014.01.081. Epub 2014 Mar 2.
PubMed 24598724Sinnenberg L, Buttenheim AM, Padrez K, Mancheno C, Ungar L, Merchant RM. Twitter as a Tool for Health Research: A Systematic Review. Am J Public Health. 2017 Jan;107(1):e1-e8. doi: 10.2105/AJPH.2016.303512. Epub 2016 Nov 17.
PubMed 27854532Milinovich GJ, Williams GM, Clements AC, Hu W. Internet-based surveillance systems for monitoring emerging infectious diseases. Lancet Infect Dis. 2014 Feb;14(2):160-8. doi: 10.1016/S1473-3099(13)70244-5. Epub 2013 Nov 28.
PubMed 24290841Devlin J, Chang M-W, Lee K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint, 2018, arXiv:181004805.
Larson HJ, Jarrett C, Schulz WS, Chaudhuri M, Zhou Y, Dube E, Schuster M, MacDonald NE, Wilson R; SAGE Working Group on Vaccine Hesitancy. Measuring vaccine hesitancy: The development of a survey tool. Vaccine. 2015 Aug 14;33(34):4165-75. doi: 10.1016/j.vaccine.2015.04.037. Epub 2015 Apr 18.
PubMed 25896384Blei DM, Ng AY, Jordan MI. Latent dirichlet allocation. J Mach Learn Res, 2003, 3:993-1022.
Pennebaker J, Boyd R, Jordan K, et al. The development and psychometric properties of LIWC2015. Austin, TX: University of Texas at Austin, 2015.
Zhao N, Jiao D, Bai S, Zhu T. Evaluating the Validity of Simplified Chinese Version of LIWC in Detecting Psychological Expressions in Short Texts on Social Network Services. PLoS One. 2016 Jun 20;11(6):e0157947. doi: 10.1371/journal.pone.0157947. eCollection 2016.
PubMed 27322382Stone JA, Can SH. Linguistic analysis of municipal twitter feeds: Factors influencing frequency and engagement. Gov Inf Q, 2020, 37(4): 101468.
de Figueiredo A, Simas C, Karafillakis E, Paterson P, Larson HJ. Mapping global trends in vaccine confidence and investigating barriers to vaccine uptake: a large-scale retrospective temporal modelling study. Lancet. 2020 Sep 26;396(10255):898-908. doi: 10.1016/S0140-6736(20)31558-0. Epub 2020 Sep 10.
PubMed 32919524Szilagyi PG, Thomas K, Shah MD, Vizueta N, Cui Y, Vangala S, Kapteyn A. National Trends in the US Public's Likelihood of Getting a COVID-19 Vaccine-April 1 to December 8, 2020. JAMA. 2020 Dec 29;325(4):396-8. doi: 10.1001/jama.2020.26419. Online ahead of print.
PubMed 33372943Larson HJ, de Figueiredo A, Xiahong Z, Schulz WS, Verger P, Johnston IG, Cook AR, Jones NS. The State of Vaccine Confidence 2016: Global Insights Through a 67-Country Survey. EBioMedicine. 2016 Oct;12:295-301. doi: 10.1016/j.ebiom.2016.08.042. Epub 2016 Sep 13.
PubMed 27658738Abd-Alrazaq A, Alhuwail D, Househ M, Hamdi M, Shah Z. Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study. J Med Internet Res. 2020 Apr 21;22(4):e19016. doi: 10.2196/19016.
PubMed 32287039
Study at a glance
From the registry record, last updated by the sponsor on 5 Jul 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Social Media-based Machine Learning Study to Monitor Vaccine Confidence and Hesitancy and Early Warn Emerging Vaccine-related Risks in Real Time
- ClinicalTrials.gov ID
- NCT05442762
- Acronym / sponsor ID
- ECT2112016948
- Conditions
- Social Medium, Data Collection, Patient Acceptance of Health Care, Immunization Programs, Vaccination Refusal, Information Dissemination
- Keywords
- vaccination hesitancy, vaccine confidence, social media, infodemiology, machine learning
- Study type
- Observational
- Recruitment status
- WithdrawnReason stopped: Adjustment of research plan
- Enrolment
- 0 participants (actual)
- Lead sponsor
- Fudan UniversityUniversity, hospital or other
- Collaborators
- Merck Sharp & Dohme LLC
- Study start
- 1 Mar 2022 (actual)
- Primary completion
- 1 Jun 2022 (actual)
- Study completion
- 24 Jun 2022 (actual)
- First posted
- 5 Jul 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
History and scientific evidence show that it is critical to maintain public trust and confidence in vaccination. Any crisis in confidence has the potential to cause significant disruption and a detrimental impact on vaccination. Vaccine hesitancy is a complex and context-specific issue that varies across time, place, and vaccines. It has been cited by World Health Organization(WHO) as one of the top ten threats to global health in 2019. Coronavirus disease(COVID-19) pandemic may change public confidence in vaccines. Therefore, it is necessary to establish a surveillance system to monitor vaccine confidence and hesitancy in real time.
To date, a growing body of literature has used social media platforms such as Twitter and weico for public health research. Large amounts of real time data posted on social media platforms can be used to quickly identify the public's attitudes on vaccines, as a way to support health communication and health promotion, messaging. However, textual data on social media is difficult to be analyzed. Recent progress in machine learning makes it possible to automatically analyze textual data on social media in real time.
In this study, the investigators will establish a social media surveillance and analysis platform on vaccines, develop a series of machine learning models to monitor vaccine confidence and early detect emerging vaccine-related risks, and assess public communication around vaccines. The investigators will assess the temporal and spatial distribution of vaccine confidence and hesitancy globally using Twitter data and in China using weico data, for all vaccines and Human Papilloma Virus(HPV) vaccine, respectively. Our study will guide the design of effective health communication strategies to improve vaccine confidence.
Detailed description
- Collect and update social media data regarding vaccines The investigators will automatically collect all social media posts regarding vaccines in real time. Social media cohort database will be established and updated for all vaccines and Human Papilloma Virus(HPV) vaccine, respectively.
- Monitor vaccine confidence and hesitancy in real time: deep (supervised) machine learning models Deep learning model, a supervised machine learning technique, will be used to analyze text data on social media in real time according to the predefined vaccine confidence and hesitancy framework. The investigators will first manually annotate a subset of social media posts (20,000 posts) regarding vaccines. The initial manually-annotated posts are then used to train and evaluate deep learning models. Deep learning models with the best performance are selected and applied to classify all vaccine-related posts according to the vaccine confidence and hesitancy framework.
- Monitor emerging concerns and sentiment swings in real time to early warn vaccine-related risks or crises: topic (unsupervised) machine learning models and linguistic analysis There are some topics outside of the predefined vaccine confidence and hesitancy framework used in deep learning models, and new topics emerge in any time. Vaccine crisis would influence public sentiments. Monitoring emerging topics and sentiment swings will provide early warning of vaccine-related risks or crises. Use Topic Modeling, an unsupervised machine learning technique that can automatically classify text to representative topics in social media, to monitor emerging topics and concerns regarding vaccines.
- Assess public engagement on social media to inform effective health communication strategies: social media engagement analysis Besides posts data on social media, engagement data of posts are also available to be analyzed, including likes, comments, and shares of posts. The investigators will conduct social media engagement analysis to investigate public communication around vaccines online. This will guide the design of effective health communication strategies.
- Establish social media surveillance and analysis platform for vaccine confidence and crisis Through the steps above, the investigators will establish a social media surveillance and analysis platform for vaccine confidence and crisis. Time-series trends, geographic variation, and associated factors of the indicators produced above will be presented to monitor vaccine confidence in real time, early warn emerging risks or crises, and inform effective health communication strategies.
- Past research experience The investigators have conducted a series of relevant studies to analyze social media data using machine learning techniques during the COVID-19 epidemic, covering COVID-19 vaccine confidence and public response to COVID-19. These experiences make the current study feasible.
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Global Database of Vaccine Related Posts | – | –Tweets in English from Twitter and posts from weico from 2015 to 2022 for all vaccines. The investigators only included posts from individual accounts and excluded those from news, organizational accounts, or verified users. |
| Global Database of HPV Vaccine Related Posts | – | –Tweets in English from Twitter and posts from weico from 2015 to 2022 for HPV vaccine. The investigators only included posts from individual accounts and excluded those from news, organizational accounts, or verified users. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Vaccine-related posts on social media are the object of our research to assess public's vaccine confidence and hesitancy. Each post record comprises account name, profiles, contents, post time, the number of followers, and engagement data.
Key inclusion criteria (4 listed)
- Tweets and weico posts related to vaccines
- Published in 2015-2022
- English tweets
- Tweets/posts from personal accounts.
Key exclusion criteria (2 listed)
- Tweets/posts from news, organization accounts, or authenticated users
- Non English tweets.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Changes in the prevalence of vaccine confidence and hesitancy
Time frame: Change from baseline prevalence of vaccine confidence and vaccine hesitancy at 1 year.
Vaccine confidence refers to the public's tweets expressing trust in the safety and effectiveness of vaccine, recognition of the vaccination necessity, and vaccine acceptance. Vaccine hesitancy means that the tweets express vaccine-related misinformation and rumors, worry about the safety and effectiveness of the vaccine, and vaccine rejection. The investigators will calculate the ratio of these two categories in all vaccine-related tweets as the prevalence of vaccine confidence and vaccine hesitancy.
2 secondary outcomes
Changes in the prevalence of machine-generated topics
Time frame: Change from baseline prevalence of machine-generated topics at 1 year.
Changes in the public engagement on social media
Time frame: Change from baseline public engagement on social media at 1 year.
Results and safety
The study was withdrawn before enrolling participants, so there are no results.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Jul 2022.
- Background: PubMed 25896383MacDonald NE; SAGE Working Group on Vaccine Hesitancy. Vaccine hesitancy: Definition, scope and determinants. Vaccine. 2015 Aug 14;33(34):4161-4. doi: 10.1016/j.vaccine.2015.04.036. Epub 2015 Apr 17.
- Background: PubMed 24598724Larson HJ, Jarrett C, Eckersberger E, Smith DM, Paterson P. Understanding vaccine hesitancy around vaccines and vaccination from a global perspective: a systematic review of published literature, 2007-2012. Vaccine. 2014 Apr 17;32(19):2150-9. doi: 10.1016/j.vaccine.2014.01.081. Epub 2014 Mar 2.
- Background: PubMed 27854532Sinnenberg L, Buttenheim AM, Padrez K, Mancheno C, Ungar L, Merchant RM. Twitter as a Tool for Health Research: A Systematic Review. Am J Public Health. 2017 Jan;107(1):e1-e8. doi: 10.2105/AJPH.2016.303512. Epub 2016 Nov 17.
- Background: PubMed 24290841Milinovich GJ, Williams GM, Clements AC, Hu W. Internet-based surveillance systems for monitoring emerging infectious diseases. Lancet Infect Dis. 2014 Feb;14(2):160-8. doi: 10.1016/S1473-3099(13)70244-5. Epub 2013 Nov 28.
- BackgroundDevlin J, Chang M-W, Lee K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint, 2018, arXiv:181004805.
- Background: PubMed 25896384Larson HJ, Jarrett C, Schulz WS, Chaudhuri M, Zhou Y, Dube E, Schuster M, MacDonald NE, Wilson R; SAGE Working Group on Vaccine Hesitancy. Measuring vaccine hesitancy: The development of a survey tool. Vaccine. 2015 Aug 14;33(34):4165-75. doi: 10.1016/j.vaccine.2015.04.037. Epub 2015 Apr 18.
- BackgroundBlei DM, Ng AY, Jordan MI. Latent dirichlet allocation. J Mach Learn Res, 2003, 3:993-1022.
- BackgroundPennebaker J, Boyd R, Jordan K, et al. The development and psychometric properties of LIWC2015. Austin, TX: University of Texas at Austin, 2015.
- Background: PubMed 27322382Zhao N, Jiao D, Bai S, Zhu T. Evaluating the Validity of Simplified Chinese Version of LIWC in Detecting Psychological Expressions in Short Texts on Social Network Services. PLoS One. 2016 Jun 20;11(6):e0157947. doi: 10.1371/journal.pone.0157947. eCollection 2016.
- BackgroundStone JA, Can SH. Linguistic analysis of municipal twitter feeds: Factors influencing frequency and engagement. Gov Inf Q, 2020, 37(4): 101468.
- Results publication: PubMed 32919524de Figueiredo A, Simas C, Karafillakis E, Paterson P, Larson HJ. Mapping global trends in vaccine confidence and investigating barriers to vaccine uptake: a large-scale retrospective temporal modelling study. Lancet. 2020 Sep 26;396(10255):898-908. doi: 10.1016/S0140-6736(20)31558-0. Epub 2020 Sep 10.
- Results publication: PubMed 33372943Szilagyi PG, Thomas K, Shah MD, Vizueta N, Cui Y, Vangala S, Kapteyn A. National Trends in the US Public's Likelihood of Getting a COVID-19 Vaccine-April 1 to December 8, 2020. JAMA. 2020 Dec 29;325(4):396-8. doi: 10.1001/jama.2020.26419. Online ahead of print.
- Individual participant dataSharing planned: No. According to the terms of the agreement, individual data can not be shared.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Social Medium and Data Collection, run by Fudan University.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: withdrawn, as of the sponsor's last update on 5 Jul 2022. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study was withdrawn before enrolling participants, so there are no results.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Changes in the prevalence of vaccine confidence and hesitancy (time frame: Change from baseline prevalence of vaccine confidence and vaccine hesitancy at 1 year.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05442762, which also lists 14 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 5 Jul 2022.