PUB ASSET- Streamlining Systematic Reviews (1)
                     STREAMLINING SYSTEMATIC 
SRIEMVPIELWIFYSI:NG DATA EXTRACTION AND 
BIAS ASSESSMENT WITH CHARMS 
AND PROBAST.
An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, Pubrica
Group:  www.pubrica.com
Email: [email protected]
In research, systematic reviews are like guiding stars, leading 
us to evidence-based conclusions. Working with an abundance 
of data can be challenging, but CHARMS (Checklist for critical 
Appraisal and data extraction for systematic Reviews of 
prediction Modelling Studies) and PROBAST (Prediction model 
Risk of Bias Assessment Tool) can help.
Data extraction is like gathering supplies for a journey, while 
bias assessment is like charting a safe course. CHARMS helps 
gather data efficiently, like a well-prepared sailor stocking up 
on provisions. Meanwhile, PROBAST acts as a lookout, helping 
spot and avoid biases that could throw us off course. These 
frameworks simplify our tasks, making research smoother. 
DATA EXTRACTION AND BIAS ASSESSMENT IN RESEARCH: THE ROLE OF 
TOOLS LIKE CHARMS AND PROBAST
In the systematic review process, data extraction means collecting important 
information from different studies and organizing it neatly. This can take a lot 
of time, and mistakes can happen, especially when there are many studies to 
go through. Researchers often need help gathering data in the same way 
across different reviewers and making sure it’s accurate and complete.
Bias assessment is about checking if there are any factors in the studies that 
could make the results less reliable. This step is important to determine 
whether the evidence is trustworthy. It takes work because it involves 
understanding research methods and statistics.
To make these tasks easier, researchers have created tools like CHARMS and 
PROBAST. CHARMS helps collect data from prediction modeling studies in a 
standard way, and PROBAST helps check if prediction models have biases. These 
tools provide clear steps to follow, which saves time and ensures the review is 
done well. They help researchers be consistent and reduce mistakes.
HOW CHARMS SIMPLIFIES THE 
PROCESS 
CAHNARDM SIM prPovRidOesV aE sStru CctuOreNd SapISprToaEchN tCo Ydata extraction, ensuring 
that all relevant information is captured consistently. It helps 
researchers collect data from prediction modeling studies in a 
constructed way. It has a checklist that guides reviewers on what 
information to gather. This checklist ensures that everything important 
iOsn neo gtirceeadt,  tmhiankgi nagb othuet  CreHsAuRltMs Sm iosr eth tartu sitt wmoarktehsy. sure all reviewers 
follow the same standards. This reduces differences between 
reviewers, which is important in systematic reviews where many people 
CarHeA iRnMvoSl vise da.lso flexible and can be used for different types of studies. 
It’s not limited to one field, so it’s useful for various research questions.
CHARMS helps with data extraction and estimates bias in studies. It 
includes a feature to detect bias, which enhances transparency and 
accountability in the review process.
HOW PROBAST STREAMLINES BIAS 
ASSESSMENT AND ENHANCES VALIDITY.
PROBAST helps researchers check for bias in prediction model 
studies. It looks at four areas: who was chosen for the study, 
the factors being predicted, the outcomes measured, and how 
the analysis was done. By systematically examining these 
areas, researchers can spot biases that affect the prediction 
mOnoed eglr’es actr ethdiinbgil iatyb.out PROBAST is that it guides researchers 
through the evaluation process. It provides clear questions for 
each area, making sure everything important is noticed.
Using PROBAST helps researchers check for bias thoroughly, 
making their reviews more reliable. It makes sure biases aren’t 
overlooked, which makes the findings more reliable for making 
decisions based on evidence.
STEP-BY-STEP GUIDE ON IMPLEMENTING CHARMS AND PROBAST IN 
SYSTEMATIC REVIEWS
1.Get to Know CHARMS: CHARMS is a helpful tool for critical appraisal and data extraction in prediction modeling 
studies. It comprises a checklist for systematic reviews that ensures the accurate collection of essential 
information.
 2.Learn about PROBAST: PROBAST evaluates prediction model study bias using signaling questions. Familiarize 
yourself with its domains to assess study quality effectively.
3.Customize CHARMS and PROBAST: Adapt these tools to match your review’s objectives and questions. Make 
sure to cover all relevant aspects and biases specific to your study.
4.Create a Data Extraction Form: Develop a structured form based on CHARMS elements to extract data 
consistently from all included studies. Include study details, bias domains, and other relevant info.
5.Train Your Reviewers: Training on CHARMS and PROBAST is necessary for consistent and accurate reviews.
6.Use CHARMS for Extraction: Gather data from each study using your customized data extraction form. This 
method guarantees the capture of all the essential information.
7.Employ PROBAST for Bias Assessment: Use PROBAST to assess bias in prediction models. Evaluate each 
domain and potential biases with it signaling questions.
8.Document Your Findings: Record your data extraction and bias assessment findings using CHARMS and 
PROBAST. This documentation will be essential for your review process and any future updates.
In conclusion, integrating CHARMS and PROBAST into systematic reviews offers a powerful solution for 
researchers seeking efficiency and accuracy. CHARMS ensures consistent data extraction, while 
PROBAST facilitates thorough bias assessment, streamlining the review process. By simplifying these 
critical tasks, researchers can enhance the reliability and transparency of their findings, ultimately 
advancing evidence-based decision-making in various fields. With their structured approaches and 
comprehensive frameworks, CHARMS and PROBAST stand as invaluable tools in the pursuit of robust 
sGyestt eremliaatbicle r seyvsietewms.atic review services from Pubrica! Our experts will gather and analyze research 
thoroughly, giving you insightful reviews you can trust. Let’s simplify your review process – reach out 
to us today!
REFERENC
1.FernandEez-Felix, B. M., López-Alcalde, J., Roqué, M., Muriel, A., & Zamora, J. (2023). CHARMS 
and PROBAST at your fingertips: a template for data extraction and risk of bias assessment in 
systematic reviews of predictive models. BMC Medical Research Methodology, 23(1), 1-8.
2.Kuo, R. Y., Harrison, C., Curran, T. A., Jones, B., Freethy, A., Cussons, D., … & Furniss, D. 
(2022). Artificial intelligence in fracture detection: a systematic review and meta-analysis. 
Radiology, 304(1), 50-62.
3.Kraft, S. A., McMullen, C., Lindberg, N. M., Bui, D., Shipman, K., Anderson, K., … & Lee, S. S. J. 
(2020). Integrating stakeholder feedback in translational genomics research: an ethnographic 
analysis of a study protocol’s evolution. Genetics in Medicine, 22(6), 1094-1101
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