PainANNNCT02682875
The Evaluation With Artificial Neural Network of Pain Scales in Children (ANN)
An observational study of Pain, run by Cukurova University at 1 site in Turkey (Türkiye).
Trial summary
The study evaluates with Artificial Neural Network (ANN) of pain scales in children. Pain of these patients' will be evaluated by many pain scales in the postoperative period. Read more about this study
- Who is it for?
- Ages 2 months to 18 yearsHealthy volunteers can take part
- Conditions the study is for
- Pain
- How many people
- 140 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- Nov 2015to May 2016
- How long will I take part?
- 4 Months
- 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 2 months to 18 years
- All sexes
- Healthy volunteers accepted
- Child, Adult
Study population: pediatric patients between 2 months to 18 years undergoing any surgery was included on this study. Pain of these patients' will be evaluated by many pain scales in the postoperative period. These scales include OUCHER, Visiuel Analog Scale, FLACC, Faces Pain Scale revise, Wong Baker Faces Scale, Faces Pain Scale, Numeric rating Scale, Verbal Rating Scale, CHEOPS and also age, blood pressure, respiratuar rate, heart rate will be recorded.
Who may be able to join
- 2 months -18 years
- Children undergoing elective surgery
Who cannot join
- Children with mental and motor development retardation
- Patients scheduled emergency surgery
- Patients and parents, who refused to participate in this study
Trial locations and contacts
1 site in 1 country.
Turkey (Türkiye)1 site, 1 recruiting
- Recruiting
Zehra Hatipoğlu
Adana
Zehra Hatipoğlu, 05324471670, hatipogluzehra@gmail.com
About this study
Official title: The Evaluation With Artificial Neural Network of Pain Scales in Children
The study evaluates with Artificial Neural Network (ANN) of pain scales in children. Pain of these patients' will be evaluated by many pain scales in the postoperative period.
Read the detailed description
Pain of these patients' will be evaluated by many pain scales in the postoperative period. These scales include OUCHER, Visiuel Analog Scale, FLACC, Faces Pain Scale revise, Wong Baker Faces Scale, Faces Pain Scale, Numeric rating Scale, Verbal Rating Scale, CHEOPS and also age, blood pressure, respiratuar rate, heart rate will be recorded.
In the first step of this study, the parameters taken into account on widely used each pain scale practically and importance level of each parameter will be examined. The parameters taken into account in determining the degree of pain and degree of each pain scale will be recorded. Independent t-test of the pain scales will be analyzed whether different statistically. Pain scales used in the application at conclusion statistical analysis will be grouped.
In addition, parameters considered to be effective on pain (pulse, blood pressure, etc.) will be determined and also recorded. The degree of importance on pain of the current scales and other parameters to be determined and as result, new pain scale will be created. While determining the the level of importance will be utilized from the analytic hierarchy process (AHP). Making binary comparisons between AHP and parameters, the level of importance and weightiness score of each parameter will be determined. The mathematical formulation of the pain points will be presented considering AHP weightiness score of each parameter.
Although pain scores obtained mathematically, pain score with Artificial Neural Network (ANN) will be estimated considering the parameters that impact on the pain score.
The final step, the creation of new pain scale comparing the pain scores obtained by mathematical formulation and Artificial Neural Network and, it is intended to be compared with the current scales.
- Conditions
- Pain
- Medical subject headings
- Pain
What is being measured
The primary outcome is the main question the study is designed to answer.
Artificial Neural Network
the creation of new pain scale
Measured: 4 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Feb 2016 (estimated)
- Study startNov 2015
- Primary completionMar 2016 (estimated)
- Study completionMay 2016 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for PainStudies of the same condition, open and under way.
- More trials run by Cukurova 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
- Case only
- Time perspective
- Prospective
- Enrolment
- 140 participants (estimated)
- Target duration
- 4 Months
Sponsor and investigators
- Lead sponsor
- Cukurova University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Zehra, Cukurova University
- Investigators
- Dilek Özcengiz, Principal investigator, Cukurova University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT02682875
- Sponsor's protocol ID
- Zehra123
- Acronym
- ANN
- Status last verified
- Feb 2016
- First submitted
- 27 Jan 2016
- Last updated
- 17 Feb 2016
- Results posted
- No
- Data monitoring committee
- No
- Individual data sharing
- Yes
Publications
Azimi P, Mohammadi HR, Benzel EC, Shahzadi S, Azhari S, Montazeri A. Artificial neural networks in neurosurgery. J Neurol Neurosurg Psychiatry. 2015 Mar;86(3):251-6. doi: 10.1136/jnnp-2014-307807. Epub 2014 Jul 1.
PubMed 24987050
Study at a glance
From the registry record, last updated by the sponsor on 17 Feb 2016. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Evaluation With Artificial Neural Network of Pain Scales in Children
- ClinicalTrials.gov ID
- NCT02682875
- Acronym / sponsor ID
- ANN / Zehra123
- Conditions
- Pain
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 140 participants (estimated)
- Lead sponsor
- Cukurova UniversityUniversity, hospital or other
- Study start
- Nov 2015
- Primary completion
- Mar 2016 (estimated)
- Study completion
- May 2016 (estimated)
- First posted
- 17 Feb 2016 (estimated)
- 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 evaluates with Artificial Neural Network (ANN) of pain scales in children. Pain of these patients' will be evaluated by many pain scales in the postoperative period.
Detailed description
Pain of these patients' will be evaluated by many pain scales in the postoperative period. These scales include OUCHER, Visiuel Analog Scale, FLACC, Faces Pain Scale revise, Wong Baker Faces Scale, Faces Pain Scale, Numeric rating Scale, Verbal Rating Scale, CHEOPS and also age, blood pressure, respiratuar rate, heart rate will be recorded.
In the first step of this study, the parameters taken into account on widely used each pain scale practically and importance level of each parameter will be examined. The parameters taken into account in determining the degree of pain and degree of each pain scale will be recorded. Independent t-test of the pain scales will be analyzed whether different statistically. Pain scales used in the application at conclusion statistical analysis will be grouped.
In addition, parameters considered to be effective on pain (pulse, blood pressure, etc.) will be determined and also recorded. The degree of importance on pain of the current scales and other parameters to be determined and as result, new pain scale will be created. While determining the the level of importance will be utilized from the analytic hierarchy process (AHP). Making binary comparisons between AHP and parameters, the level of importance and weightiness score of each parameter will be determined. The mathematical formulation of the pain points will be presented considering AHP weightiness score of each parameter.
Although pain scores obtained mathematically, pain score with Artificial Neural Network (ANN) will be estimated considering the parameters that impact on the pain score.
The final step, the creation of new pain scale comparing the pain scores obtained by mathematical formulation and Artificial Neural Network and, it is intended to be compared with the current scales.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
- Target follow-up
- 4 Months
Who may qualify
- Ages
- 2 months to 18 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult
- Study population
- pediatric patients between 2 months to 18 years undergoing any surgery was included on this study. Pain of these patients' will be evaluated by many pain scales in the postoperative period. These scales include OUCHER, Visiuel Analog Scale, FLACC, Faces Pain Scale revise, Wong Baker Faces Scale, Faces Pain Scale, Numeric rating Scale, Verbal Rating Scale, CHEOPS and also age, blood pressure, respiratuar rate, heart rate will be recorded.
Key inclusion criteria (2 listed)
- 2 months -18 years
- Children undergoing elective surgery
Key exclusion criteria (3 listed)
- Children with mental and motor development retardation
- Patients scheduled emergency surgery
- Patients and parents, who refused to participate in this study
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
Artificial Neural Network
Time frame: 4 months
the creation of new pain scale
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Feb 2016.
- Results publication: PubMed 24987050Azimi P, Mohammadi HR, Benzel EC, Shahzadi S, Azhari S, Montazeri A. Artificial neural networks in neurosurgery. J Neurol Neurosurg Psychiatry. 2015 Mar;86(3):251-6. doi: 10.1136/jnnp-2014-307807. Epub 2014 Jul 1.
- Artificial neural networks in neurosurgeryLink 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.
Dilek Özcengiz principal investigator
Cukurova University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Pain, run by Cukurova University at 1 site in Turkey (Türkiye).
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 2 inclusion and 3 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: status not confirmed, as of the sponsor's last update on 17 Feb 2016. 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 Turkey (Türkiye). 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Dilek Özcengiz (05324673220, dilekozcengiz@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Artificial Neural Network (time frame: 4 months). 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 NCT02682875, which also lists 1 linked publication.
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 17 Feb 2016.