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Critiplot.

A Critical Appraisal Plot Visualiser for Risk of Bias Assessments

About Critiplot

Critiplot is a comprehensive web tool for visualizing multiple risk-of-bias assessment tools.
It helps researchers generate traffic light plots and weighted bar plots for various assessment methodologies including NOS, GRADE, ROBIS, and JBI tools.
Built on Python, it complements evidence synthesis workflows while ensuring reproducibility and consistent visualization of your datasets.

Critiplot supports the following assessment tools:

  • NOS (Newcastle-Ottawa Scale) - For assessing the quality of non-randomized studies
  • GRADE - For rating the certainty of evidence in systematic reviews
  • ROBIS - For assessing risk of bias in systematic reviews
  • JBI Case Report - For critical appraisal of case reports
  • JBI Case Series - For critical appraisal of case series

Key features of Critiplot include:

  • Generation of intuitive traffic light plots for each study's risk-of-bias assessment.
  • Weighted bar plots to summarize overall domain-level distributions across studies.
  • Multiple visualization themes for each assessment tool.
  • Publication-ready visualizations suitable for manuscripts, presentations, and reports.
  • Open-source design to facilitate reproducibility and transparency in evidence synthesis.

With Critiplot, researchers can quickly interpret and communicate study-level risk-of-bias information across multiple assessment frameworks, ensuring both clarity and scientific rigor.

Python Package

Critiplot is also available as a Python package for local use. You can install it via pip: 

pip install critiplot

Once installed, you can use Critiplot in your Python scripts to generate risk of bias visualizations programmatically.

Benefits of using the Python package:

  • Integrate Critiplot directly into your data analysis workflows
  • Automate visualization generation for large datasets
  • Customize plots with advanced parameters
  • Use Critiplot in Jupyter notebooks and other Python environments

For documentation and examples, visit the GitHub repository or the PyPI page.

Result 1 Result 2 Result 3 NOS Result NOS Result 2 GRADE Result 1 GRADE Result 2 GRADE Result 3 ROBIS Result 1 ROBIS Result 2 ROBIS Result 3 ROBIS Result 4 ROBIS Result 5 Case Report Case Report 1 Case Report 2 Case Report 3 Case Report 4 Case Report Series Plot Series Plot 1 Series Plot 2 Series Plot 4 Series Plot 5
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Data Structure for Assessment Tools

Data Templates

Download ready-to-use templates for each assessment tool. Simply click to download the CSV or Excel format.

NOS

CSV Excel

GRADE

CSV Excel

ROBIS

CSV Excel

JBI Case Report

CSV Excel

JBI Case Series

CSV Excel

NOS (Newcastle-Ottawa Scale)

To work correctly with Critiplot for NOS assessments, your uploaded table should follow this structure:

  • First column: Study details (Author, Year)
  • Domain columns: Each additional column corresponds to a specific NOS domain:
    • Representativeness
    • Non-exposed Selection
    • Exposure Ascertainment
    • Outcome Absent at Start
    • Comparability (Age/Gender)
    • Comparability (Other)
    • Outcome Assessment
    • Follow-up Length
    • Follow-up Adequacy
    • Total Score: Sum of the domain scores
    • Overall RoB: Overall risk-of-bias judgement for each study (Low, Moderate, High)
Author, Year Representativeness Non-exposed Selection Exposure Ascertainment Outcome Absent at Start Comparability (Age/Gender) Comparability (Other) Outcome Assessment Follow-up Length Follow-up Adequacy Total Score Overall RoB
Study 1, 2019 1 1 1 1 1 0 1 1 1 8 Low
Study 2, 2024 1 1 1 1 1 0 1 1 0 7 Moderate

GRADE

For GRADE assessments, your uploaded table should follow this structure:

  • Outcome: Name of the outcome
  • Study: Study identifier
  • Risk of Bias: Risk of bias judgement (High, Moderate, Low, Very Low, None)
  • Inconsistency: Inconsistency judgement (High, Moderate, Low, Very Low, None)
  • Indirectness: Indirectness judgement (High, Moderate, Low, Very Low, None)
  • Imprecision: Imprecision judgement (High, Moderate, Low, Very Low, None)
  • Publication Bias: Publication bias judgement (High, Moderate, Low, Very Low, None)
  • Overall Certainty: Overall certainty judgement (High, Moderate, Low, Very Low)
Outcome Study Risk of Bias Inconsistency Indirectness Imprecision Publication Bias Overall Certainty
Mortality Study 1 2020 Low Low Low Moderate None Low
Infection Study 2 2019 Moderate High Low Low Low Moderate

ROBIS

For ROBIS assessments, your uploaded table should follow this structure:

  • Review: Review identifier
  • Study Eligibility: Risk of bias judgement (Low, Unclear, High)
  • Identification & Selection: Risk of bias judgement (Low, Unclear, High)
  • Data Collection: Risk of bias judgement (Low, Unclear, High)
  • Synthesis & Findings: Risk of bias judgement (Low, Unclear, High)
  • Overall Risk: Overall risk of bias judgement (Low, Unclear, High)
Review Study Eligibility Criteria Identification & Selection of Studies Data Collection & Study Appraisal Synthesis & Findings Overall RoB
Study 1 2021 Low High Unclear Low High
Study 2 2020 Low Low Low Low Low

JBI Case Report

For JBI Case Report assessments, your uploaded table should follow this structure:

  • Author,Year: Study identifier (e.g., "Smith, 2020")
  • Demographics: Score (0 or 1)
  • History: Score (0 or 1)
  • ClinicalCondition: Score (0 or 1)
  • Diagnostics: Score (0 or 1)
  • Intervention: Score (0 or 1)
  • PostCondition: Score (0 or 1)
  • AdverseEvents: Score (0 or 1)
  • Lessons: Score (0 or 1)
  • Total: Sum of the domain scores
  • Overall RoB: Overall risk of bias judgement (Low, High)
Author,Year Demographics History ClinicalCondition Diagnostics Intervention PostCondition AdverseEvents Lessons Total Overall RoB
Study 1,2022 1 1 0 1 1 1 1 1 7 Low
Study 2,2021 1 0 0 1 0 0 0 1 3 High

JBI Case Series

For JBI Case Series assessments, your uploaded table should follow this structure:

  • Author,Year: Study identifier (e.g., "Smith, 2020")
  • InclusionCriteria: Score (0 or 1)
  • StandardMeasurement: Score (0 or 1)
  • ValidIdentification: Score (0 or 1)
  • ConsecutiveInclusion: Score (0 or 1)
  • CompleteInclusion: Score (0 or 1)
  • Demographics: Score (0 or 1)
  • ClinicalInfo: Score (0 or 1)
  • Outcomes: Score (0 or 1)
  • SiteDescription: Score (0 or 1)
  • Statistics: Score (0 or 1)
  • Total: Sum of the domain scores
  • Overall RoB: Overall risk of bias judgement (Low, High)
Author,Year InclusionCriteria StandardMeasurement ValidIdentification ConsecutiveInclusion CompleteInclusion Demographics ClinicalInfo Outcomes SiteDescription Statistics Total Overall RoB
Study 1,2022 1 1 1 1 1 1 1 1 1 1 10 Low
Study 2,2021 1 0 1 1 0 1 0 1 0 0 5 High

Support & Contact

Have questions or need assistance with Critiplot? This tool is designed for reproducible research, and we are here to help you integrate it into your workflow.

You can open a GitHub Issue or send an email directly for guidance, troubleshooting, or feature requests.

Citation

If you use Critiplot to create risk-of-bias plots for your study, please remember to cite the tool.

Sahu, V. (2025). Critiplot: A Critical Appraisal Plot Visualiser for Risk of Bias in Systematic Reviews and Meta-Analyses (v1.0.3). Zenodo. https://doi.org/10.5281/zenodo.17236600