Artificial Intelligence (AI)NCT07051343
Chatbot About Electronic Fetal Monitoring
A study testing designed artificial intelligence Chatbot education about electronic fetal monitoring and traditional teaching method in people with Artificial Intelligence (AI) and Electronic Fetal Monitoring, run by Mansoura University at 1 site in Egypt.
Trial summary
The study aims to investigate the effect of using artificial intelligence Chatbot education about electronic fetal monitoring on maternity nursing students' performance. The aim will be achieved through the following, 1. Designing AI Chatbot about electronic fetal monitoring. 2. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Artificial Intelligence (AI), Electronic Fetal Monitoring, Nursing Students
- What is being tested?
- designed artificial intelligence Chatbot education about electronic fetal monitoring and traditional teaching method
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 84 participants
- Where
- 1 siteEgypt
- Study dates
- 1 Jun 2025to 1 Dec 2025
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
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
Who may be able to join
- third level students at faculty of nursing Mansoura university who will register midwifery course of academic year 2024/2025
Who cannot join
- students who refuse to participate in the study and those not registered in the course
Trial locations and contacts
1 site in 1 country.
Egypt1 site
Faculty of Nursing, Mansoura University
Al Mansurah, Dakahlia Governorate, 35516
About this study
Official title: Effect of Using Artificial Intelligence Chatbot About Electronic Fetal Monitoring on Maternity Nursing Students' Performance
- The study aims to investigate the effect of using artificial intelligence Chatbot education about electronic fetal monitoring on maternity nursing students' performance.
- The aim will be achieved through the following,
- Designing AI Chatbot about electronic fetal monitoring.
- Exploring the effect of using AI Chatbot about electronic fetal monitoring on students' performance, interest in education, self-directed learning \& feedback satisfaction.
- The students will be divided into two groups, the intervention group will use EFM Chatbot, and the control group will receive the traditional learning
Read the detailed description
Effect of Using Artificial Intelligence Chatbot about Electronic Fetal Monitoring on Maternity Nursing Students' Performance Rapid advances in information and technology have led to the advancement of interactive learning environments. In line with these changes, artificial intelligence (AI) has emerged as a primary area of interest. AI is the simulation of human intelligence processes by machines to maximize their chance to achieve certain goal, it has actively permeated many aspects of live.
The integration of artificial intelligence (AI) into education and research has become more prevalent in recent years. With the evolution of medical technology, content of medical and nursing education has changed; thus, continuously enhancing medical student's knowledge and capabilities is a critical educational objective.
The incorporation of AI into the education field has led to many possibilities, benefiting both educators and students. AI takes on various roles as an intelligent tutor, a learning partner, and even an adviser in influencing educational policies also, provide a focused, personalized, and result-oriented online learning environment.
The current generation of nursing students, having been raised in an era of networking \& highly familiar with internet technology. As such, it is anticipated that their learning preferences may differ from previous generations. Thus, strategies for improving students' self-directed learning, and efforts for promoting interactions between instructors and students were needed. This has led to a growing interest in using AI powered technology. Among the various forms of AI, Chatbots represent one of the most commonly encountered AI-based tools in the education field The aims of nursing training include not only mastering skills but also fostering the competence to make decisions for problem solving. Regarding essential nursing techniques in midwifery health nursing, education on installing EFM equipment and interpreting its results is required. Electronic fetal monitoring (EFM) is a method to assess fetal health, utilized to prevent fetal hypoxia and provide interventions at an early stage by observing changes in fetal heartbeat.
Since EFM-related tasks, require professional knowledge \& understanding, nursing students should be provided with sufficient learning and training in EFM prior to their training in the delivery room. With the aim of helping students to make correct decisions when dealing with real cases, it is necessary to engage them in authentic problem-solving contexts Traditional education system faces several issues, including overcrowded classrooms, high student teacher ratio, lack of personalized attention for students, varying learning paces and styles. Consequently, the lack of individualized student support leads to low satisfaction with learning and subsequent weak learning efficiency.
Based on Egypt vision 2030 one of Challenges in the Health science Sector is mismatch of skills between higher education graduates and the needs of the health sector. Given that the priority agenda of the Egyptian government is reducing the high Maternal\& neonatal Mortality Rate (MMR) \& (NMR), a specific set of KPI has been selected to be used to monitor progress until 2030 as decrease in MMR and increase in life expectancy at birth to reach 31% \& 75% respectively.
As the educational landscape continues to evolve, the rise of AI-powered chatbots emerges as a promising solution to enable students to learn and think deeply in the contexts of handling obstetrics. According to Egyptian National Council for Artificial Intelligence strategy, applying AI to areas such as education or healthcare can facilitate access, and reduce risks and costs.
In this context, it is essential to assess the effects of Chatbot, which have the potential to be used in educational institutions that have the mission of shaping society and guiding the future of all stakeholders of education. Therefore, developing \& using Chatbot as educational method and evaluating its effects in the field of nursing education are needed, so this study will be conducted.
- An approval will be obtained from the Faculty of Nursing Research Ethics Committee to carry out the study.
- An official permission will be obtained by submission of an official letter to head of woman's' health and midwifery nursing department Faculty of Nursing, Mansoura University, and the Dean of the selected setting to conduct the study after explaining the aim, importance and benefits of the research of the study to gain their cooperation and support during data collection.
- Students from level three will be recruited in the study \& divided into two groups, the intervention group will use EFM Chatbot, and the control group will receive the traditional learning.
- The researcher will measure the effectiveness of EFM Chatbot to assess how well the maternity students were remembering the material and applying the instructions compare the results with the control group.
- Being tested
- designed artificial intelligence Chatbot education about electronic fetal monitoring, traditional teaching method
- Keywords
- chatbot, EFM, artificial intelligence, students, electronic fetal monitor
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
designed artificial intelligence Chatbot education about electronic fetal monitoringOther
effect of using designed artificial intelligence Chatbot about electronic fetal monitoring on maternity nursing students' performance
traditional teaching methodOther
the traditional learning method (online meeting).
| Study group | Role | What they receive |
|---|---|---|
| intervention group | Experimental | this group will receive the designed electronic fetal monitoring AI Chatbot education Receives: Other: designed artificial intelligence Chatbot education about electronic fetal monitoring |
| control group | Other | this group includes students who will receive the traditional learning method (online meeting). Receives: Other: traditional teaching method |
What is being measured
The primary outcome is the main question the study is designed to answer.
Maternity nursing students who received EFM Chatbot education will have better theoretical knowledge regarding EFM within 3 months.
Maternity Students' Knowledge regard EFM will be assessed using a test made by the researcher consist of 33 questions with a varying degree of difficulty about the core knowledge regrading EFM.
Calculated scores will be assigned to the students' knowledge-related answers. Each correct response received a score of "one" \& every incorrect response received a score of "zero." The scores of the items for each area of knowledge will be added up, and the total was divided by the number of items, yielding a mean score for each area.
Classification system for the knowledge level will be:
- Good knowledge (80% or higher)
- Average knowledge (60% to 79%)
- poor knowledge (40% to 59%)
- very poor knowledge (less than 40%).
Measured: 3 months
Maternity nursing students who received EFM Chatbot education will have satisfactory practical interpretation skills regarding EFM within 3 months.
Maternity Students' Interpretation Competency regard EFM will be assessed by a test contain number of traces charts; each trace will contain questions intended to assess the respondent's understanding of it \& the ability to accurately interpret and analyze electronic signals generated by fetal cardiotocography machine (total 40 questions).
Each accurate response received a score of one, while each wrong response received a score of zero. The scores of the items will be added up for each area of fetal trace interpretation, and the total will be divided by the number of items, yielding a mean score for each region. A percentage score will be created from these scores.
A successful interpretation of the fetal trace will be considered satisfactory if the percent score was greater than 60%, as opposed to an unsatisfactory interpretation scoring
Measured: 3 months.
Maternity nursing students who received EFM Chatbot education will have better clinical reasoning confidence regarding EFM within 3 months.
Maternity students' clinical reasoning confidence in fetal health assessment: will be measured with series of questions as ability to collect patient history, apply proper assessment skills \& identify abnormalities from collected patient information…. etc. Using a 5-point Likert scale with a response of "strongly confident" and "not confident at all" accounts for 5 and 1 points, respectively.
The scores of the questions will be added up, and the total will be divided by the number of items, yielding a mean score.
The score will be stratified as:
20% to less than 35% indicates beginning 35%-60% indicates developing 61%-85% indicates achieving above 86% indicates exemplary.
Measured: 3 months.
Secondary outcomes (2)
Maternity nursing students who received EFM Chatbot education will have more interest in education than the control group within 3 months.
this outcome will be assessed by the academic motivation scale (AMS) which contains questions as (students experience pleasure while learning, students' satisfaction of accomplishment of difficult academic activities, students obtain more interesting information in Chatbot, independent learning, self-management…etc.). Questions scored on a 5-point Likert scale from 1 (does not correspond at all) to 5 (corresponds totally).
The scores of the questions will be added up, and the total will be divided by the number of items.
Score range from 14% to less than 35% indicates low motivation Score from 35% to 57% indicates average motivation Score above 57% indicates good motivation.
Measured: 3 months
Maternity nursing students who received EFM Chatbot education will have higher feedback satisfaction.
Students' Feedback Satisfaction regard using EFM Chatbot: students will be asked questions related to acceptability and learnability of Chatbot \& the degree to which users feel fulfilled with the responses and assistance provided by the Chatbot in response to their queries as (Chatbot is an acceptable way to receive information, improves access to EFM resources \& covers information needs…. etc.). Questions will be scored on a 3-point Likert scale, the possible ratings for answers ranged from 1 (not satisfied) to 3 (satisfied).
The scores of the items will be added up, and the total will be divided by the number of items.
The score range 20% to 49% indicates low satisfaction Score from 50% to 69% indicates average satisfaction Score 70% or above indicates high satisfaction.
Measured: 3 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Jul 2025
- Study start1 Jun 2025
- Primary completion1 Nov 2025 (estimated)
- Study completion1 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Artificial Intelligence (AI)Studies of the same condition, open and under way.
- More trials run by Mansoura 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Model description
- A quasi-experimental study design is utilized in this study. It's an intervention study in which subjects won't be randomly assigned to groups, this design compares two nonequivalent groups, study group will receive the intervention then will be measured twice after the intervention. The other group (control) will be measured at the same two times, but doesn't receive any intervention
- Masking
- None (open label)
- Enrolment
- 84 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Mansoura University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Amal Mohamed Talaat AbdElwahab Hassan Ahmed Aboaish, Mansoura University
- Investigators
- Amal Mohamed Talaat Abdelwahab, assistant lecturer, Principal investigator, assistant lecturer at woman's health and midwifery nursing department, faculty of nursing, mansoura universityhanan alemam, professor, Study director, head of woman's health and midwifery nursing department, faculty of nursing, mansoura university
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07051343
- Sponsor's protocol ID
- EFM chatbot
- Status last verified
- Jun 2025
- First submitted
- 6 Jun 2025
- Last updated
- 4 Jul 2025
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes
Publications
Han JW, Park J, Lee H. Analysis of the effect of an artificial intelligence chatbot educational program on non-face-to-face classes: a quasi-experimental study. BMC Med Educ. 2022 Dec 1;22(1):830. doi: 10.1186/s12909-022-03898-3.
PubMed 36457086Abdelwahab AMT, Aboraiah MIH, Elsayed HEM. Effect of using artificial intelligence chatbot about electronic fetal monitoring on maternity nursing students' performance. BMC Med Educ. 2025 Dec 18;26(1):120. doi: 10.1186/s12909-025-08391-1.
PubMed 41408216
Study at a glance
From the registry record, last updated by the sponsor on 4 Jul 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Effect of Using Artificial Intelligence Chatbot About Electronic Fetal Monitoring on Maternity Nursing Students' Performance
- ClinicalTrials.gov ID
- NCT07051343
- Acronym / sponsor ID
- EFM chatbot
- Keywords
- chatbot, EFM, artificial intelligence, students, electronic fetal monitor
- Study type
- Interventional
- Recruitment status
- Active, not recruiting
- Enrolment
- 84 participants (estimated)
- Lead sponsor
- Mansoura UniversityUniversity, hospital or other
- Study start
- 1 Jun 2025 (actual)
- Primary completion
- 1 Nov 2025 (estimated)
- Study completion
- 1 Dec 2025 (estimated)
- First posted
- 4 Jul 2025 (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.
- The study aims to investigate the effect of using artificial intelligence Chatbot education about electronic fetal monitoring on maternity nursing students' performance.
- The aim will be achieved through the following,
- Designing AI Chatbot about electronic fetal monitoring.
- Exploring the effect of using AI Chatbot about electronic fetal monitoring on students' performance, interest in education, self-directed learning \& feedback satisfaction.
- The students will be divided into two groups, the intervention group will use EFM Chatbot, and the control group will receive the traditional learning
Detailed description
Effect of Using Artificial Intelligence Chatbot about Electronic Fetal Monitoring on Maternity Nursing Students' Performance Rapid advances in information and technology have led to the advancement of interactive learning environments. In line with these changes, artificial intelligence (AI) has emerged as a primary area of interest. AI is the simulation of human intelligence processes by machines to maximize their chance to achieve certain goal, it has actively permeated many aspects of live.
The integration of artificial intelligence (AI) into education and research has become more prevalent in recent years. With the evolution of medical technology, content of medical and nursing education has changed; thus, continuously enhancing medical student's knowledge and capabilities is a critical educational objective.
The incorporation of AI into the education field has led to many possibilities, benefiting both educators and students. AI takes on various roles as an intelligent tutor, a learning partner, and even an adviser in influencing educational policies also, provide a focused, personalized, and result-oriented online learning environment.
The current generation of nursing students, having been raised in an era of networking \& highly familiar with internet technology. As such, it is anticipated that their learning preferences may differ from previous generations. Thus, strategies for improving students' self-directed learning, and efforts for promoting interactions between instructors and students were needed. This has led to a growing interest in using AI powered technology. Among the various forms of AI, Chatbots represent one of the most commonly encountered AI-based tools in the education field The aims of nursing training include not only mastering skills but also fostering the competence to make decisions for problem solving. Regarding essential nursing techniques in midwifery health nursing, education on installing EFM equipment and interpreting its results is required. Electronic fetal monitoring (EFM) is a method to assess fetal health, utilized to prevent fetal hypoxia and provide interventions at an early stage by observing changes in fetal heartbeat.
Since EFM-related tasks, require professional knowledge \& understanding, nursing students should be provided with sufficient learning and training in EFM prior to their training in the delivery room. With the aim of helping students to make correct decisions when dealing with real cases, it is necessary to engage them in authentic problem-solving contexts Traditional education system faces several issues, including overcrowded classrooms, high student teacher ratio, lack of personalized attention for students, varying learning paces and styles. Consequently, the lack of individualized student support leads to low satisfaction with learning and subsequent weak learning efficiency.
Based on Egypt vision 2030 one of Challenges in the Health science Sector is mismatch of skills between higher education graduates and the needs of the health sector. Given that the priority agenda of the Egyptian government is reducing the high Maternal\& neonatal Mortality Rate (MMR) \& (NMR), a specific set of KPI has been selected to be used to monitor progress until 2030 as decrease in MMR and increase in life expectancy at birth to reach 31% \& 75% respectively.
As the educational landscape continues to evolve, the rise of AI-powered chatbots emerges as a promising solution to enable students to learn and think deeply in the contexts of handling obstetrics. According to Egyptian National Council for Artificial Intelligence strategy, applying AI to areas such as education or healthcare can facilitate access, and reduce risks and costs.
In this context, it is essential to assess the effects of Chatbot, which have the potential to be used in educational institutions that have the mission of shaping society and guiding the future of all stakeholders of education. Therefore, developing \& using Chatbot as educational method and evaluating its effects in the field of nursing education are needed, so this study will be conducted.
- An approval will be obtained from the Faculty of Nursing Research Ethics Committee to carry out the study.
- An official permission will be obtained by submission of an official letter to head of woman's' health and midwifery nursing department Faculty of Nursing, Mansoura University, and the Dean of the selected setting to conduct the study after explaining the aim, importance and benefits of the research of the study to gain their cooperation and support during data collection.
- Students from level three will be recruited in the study \& divided into two groups, the intervention group will use EFM Chatbot, and the control group will receive the traditional learning.
- The researcher will measure the effectiveness of EFM Chatbot to assess how well the maternity students were remembering the material and applying the instructions compare the results with the control group.
Study design
- Primary purpose
- Other
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- A quasi-experimental study design is utilized in this study. It's an intervention study in which subjects won't be randomly assigned to groups, this design compares two nonequivalent groups, study group will receive the intervention then will be measured twice after the intervention. The other group (control) will be measured at the same two times, but doesn't receive any intervention
| Arm | Type | Receives |
|---|---|---|
| intervention group | Experimental | Other: designed artificial intelligence Chatbot education about electronic fetal monitoringthis group will receive the designed electronic fetal monitoring AI Chatbot education |
| control group | Other | Other: traditional teaching methodthis group includes students who will receive the traditional learning method (online meeting). |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (1 listed)
- third level students at faculty of nursing Mansoura university who will register midwifery course of academic year 2024/2025
Key exclusion criteria (1 listed)
- students who refuse to participate in the study and those not registered in the course
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| designed artificial intelligence Chatbot education about electronic fetal monitoringeffect of using designed artificial intelligence Chatbot about electronic fetal monitoring on maternity nursing students' performance | Other | intervention group |
| traditional teaching methodthe traditional learning method (online meeting). | Other | control group |
Primary outcome
Maternity nursing students who received EFM Chatbot education will have better theoretical knowledge regarding EFM within 3 months.
Time frame: 3 months
Maternity Students' Knowledge regard EFM will be assessed using a test made by the researcher consist of 33 questions with a varying degree of difficulty about the core knowledge regrading EFM. Calculated scores will be assigned to the students' knowledge-related answers. Each correct response received a score of "one" \& every incorrect response received a score of "zero." The scores of the items for each area of knowledge will be added up, and the total was divided by the number of items, yielding a mean score for each area. Classification system for the knowledge level will be: * Good knowledge (80% or higher) * Average knowledge (60% to 79%) * poor knowledge (40% to 59%) * very poor knowledge (less than 40%).
Maternity nursing students who received EFM Chatbot education will have satisfactory practical interpretation skills regarding EFM within 3 months.
Time frame: 3 months.
Maternity Students' Interpretation Competency regard EFM will be assessed by a test contain number of traces charts; each trace will contain questions intended to assess the respondent's understanding of it \& the ability to accurately interpret and analyze electronic signals generated by fetal cardiotocography machine (total 40 questions). Each accurate response received a score of one, while each wrong response received a score of zero. The scores of the items will be added up for each area of fetal trace interpretation, and the total will be divided by the number of items, yielding a mean score for each region. A percentage score will be created from these scores. A successful interpretation of the fetal trace will be considered satisfactory if the percent score was greater than 60%, as opposed to an unsatisfactory interpretation scoring
Maternity nursing students who received EFM Chatbot education will have better clinical reasoning confidence regarding EFM within 3 months.
Time frame: 3 months.
Maternity students' clinical reasoning confidence in fetal health assessment: will be measured with series of questions as ability to collect patient history, apply proper assessment skills \& identify abnormalities from collected patient information…. etc. Using a 5-point Likert scale with a response of "strongly confident" and "not confident at all" accounts for 5 and 1 points, respectively. The scores of the questions will be added up, and the total will be divided by the number of items, yielding a mean score. The score will be stratified as: 20% to less than 35% indicates beginning 35%-60% indicates developing 61%-85% indicates achieving above 86% indicates exemplary.
2 secondary outcomes
Maternity nursing students who received EFM Chatbot education will have more interest in education than the control group within 3 months.
Time frame: 3 months
Maternity nursing students who received EFM Chatbot education will have higher feedback satisfaction.
Time frame: 3 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Jul 2025.
- Results publication: PubMed 36457086Han JW, Park J, Lee H. Analysis of the effect of an artificial intelligence chatbot educational program on non-face-to-face classes: a quasi-experimental study. BMC Med Educ. 2022 Dec 1;22(1):830. doi: 10.1186/s12909-022-03898-3.
- Derived from this study: PubMed 41408216Abdelwahab AMT, Aboraiah MIH, Elsayed HEM. Effect of using artificial intelligence chatbot about electronic fetal monitoring on maternity nursing students' performance. BMC Med Educ. 2025 Dec 18;26(1):120. doi: 10.1186/s12909-025-08391-1.
- Ministry of Planning, Monitoring and Administrative Reform CairoLink provided by the sponsor.
- Individual participant dataSharing planned: Yes
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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.
Amal Mohamed Talaat Abdelwahab, assistant lecturer principal investigator
assistant lecturer at woman's health and midwifery nursing department, faculty of nursing, mansoura university
hanan alemam, professor study director
head of woman's health and midwifery nursing department, faculty of nursing, mansoura university
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing designed artificial intelligence Chatbot education about electronic fetal monitoring and traditional teaching method in people with Artificial Intelligence (AI) and Electronic Fetal Monitoring, run by Mansoura University at 1 site in Egypt.
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 1 inclusion and 1 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: under way, not looking for participants, as of the sponsor's last update on 4 Jul 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Egypt. Whether a site is taking part right now should be confirmed with that site. See every site.
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?
This study is still under way. Results are not available in the source record yet.
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?
Maternity nursing students who received EFM Chatbot education will have better theoretical knowledge regarding EFM within 3 months. (time frame: 3 months). The study lists 3 primary outcomes in all. 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 NCT07051343, which also lists 2 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 4 Jul 2025.