Critiplot is an open-source Python tool and interactive web app for visualizing risk-of-bias (RoB) assessments across multiple evidence synthesis tools:

• Newcastle-Ottawa Scale (NOS)
• JBI Critical Appraisal Checklists (Case Report / Case Series)
• GRADE certainty of evidence
• ROBIS for systematic reviews

It produces publication-ready traffic-light plots and stacked bar charts, allowing researchers to summarize study quality clearly in systematic reviews and meta-analyses.

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🔗 Interactive web app: critiplot.vercel.app

• If you want to skip the Vercel user-interface, you can directly access Streamlit: critiplot.streamlit.app

Vercel User-Inferface

Streamlit User-Inferface

📂 Code & archive (Zenodo DOI): 10.5281/zenodo.17236600

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✨ Key Features

• Converts risk-of-bias ratings into traffic-light plots.
• Generates publication-quality figures in multiple formats: .png, .pdf, .svg, .eps.
• Supports NOS, JBI (Case Report / Case Series), GRADE, and ROBIS.
• Open-source, fully reproducible, usable via Python scripts or Streamlit web app.
• Adjustable themes, figure sizes, line thickness, and legends.

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Data & Template

• Please strictly follow the Data & Template (available as .csv & excel format) as mentioned in the main Critiplot Web: critiplot.vercel.app

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📥 Installation

git clone https://github.com/aurumz-rgb/Critiplot-main.git
cd Critiplot
pip install -r requirements.txt

Tested with Python 3.10+, Matplotlib, Seaborn, and Pandas.

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⚡ Usage

1️⃣ Python Scripts

Separate scripts are available for each assessment tool:

Script	Input	Output	Notes
nos_plot.py	NOS CSV/XLSX	PNG/PDF/SVG/EPS	Traffic-light & stacked bar plots for NOS studies
jbi_case_report_plot.py	JBI Case Report CSV/XLSX	PNG/PDF/SVG/EPS	For individual case reports
jbi_case_series_plot.py	JBI Case Series CSV/XLSX	PNG/PDF/SVG/EPS	For case series studies
grade_plot.py	GRADE CSV/XLSX	PNG/PDF/SVG/EPS	Summarizes certainty of evidence
robis_plot.py	ROBIS CSV/XLSX	PNG/PDF/SVG/EPS	Summarizes systematic review risk-of-bias

Example Commands

# NOS
python3 nos_plot.py nos_data.csv nos_plot.png
python3 nos_plot.py nos_data.xlsx nos_plot.png

# JBI Case Report
python3 jbi_case_report_plot.py case_report.csv report_plot.png
python3 jbi_case_report_plot.py case_report.xlsx report_plot.png

# JBI Case Series
python3 jbi_case_series_plot.py case_series.csv series_plot.png
python3 jbi_case_series_plot.py case_series.xlsx series_plot.png

# GRADE
python3 grade_plot.py grade_data.csv grade_plot.png
python3 grade_plot.py grade_data.xlsx grade_plot.png

# ROBIS
python3 robis_plot.py robis_data.csv robis_plot.png
python3 robis_plot.py robis_data.xlsx robis_plot.png

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Optional [theme] argument for NOS, JBI Case Report / Case Series, and ROBIS: "default", "blue", "gray", "smiley", "smiley_blue"

⚠️ Note: For GRADE, these themes are not available. Instead, GRADE supports: "default", "green", "blue"

Example Usage:

# NOS
python3 nos_plot.py nos_data.csv nos_plot.png smiley_blue

# ROBIS
python3 robis_plot.py robis_data.xlsx robis_plot.png blue

# GRADE (only default/green/blue)
python3 grade_plot.py grade_data.csv grade_plot.png green

If the theme argument is omitted, the default theme will be used.

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Sample

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2️⃣ Streamlit Web App

streamlit run app.py

• Upload your CSV/XLSX file to visualize traffic-light plots.
• Select the risk-of-bias tool: NOS, JBI, GRADE, or ROBIS.
• Choose your plot theme for a publication-ready figure.
• Download plots in PNG, PDF, SVG, or EPS formats directly.

The web provides example CSV/XLSX templates for each tool to guide formatting.

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📖 Methods Notes

• RoB assessment: Follows the original scoring/checklists of each tool.
• Visualisation: Traffic-light and weighted bar plots generated with Matplotlib / Seaborn.
• Transparency: Raw scores should be included in supplementary tables.
• Reproducibility: Code and sample datasets archived via Zenodo DOI.
• Scope: Critiplot is a visualisation tool only; it does not compute risk-of-bias.

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🔹 How Scores Are Converted to Risk-of-Bias (RoB)

NOS

• Selection domain (0–4 stars): 3–4 → Low, 2 → Moderate, 0–1 → High
• Comparability domain (0–2 stars): 2 → Low, 1 → Moderate, 0 → High
• Outcome/Exposure domain (0–3 stars): 3 → Low, 2 → Moderate, 0–1 → High

JBI

• Each domain is binary: 1 = low risk, 0 = high risk (case reports & series).

GRADE

• High / Moderate / Low / Very Low certainty mapped to traffic-light colors.

ROBIS

• Domains evaluated as Low / High / Unclear risk and visualized similarly.

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📄 Citation

If you use Critiplot in your work, please cite:

APA:

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

Other formats:

Harvard, MLA, Chicago, IEEE, Vancouver (see full web for options).

Download RIS/BibTeX citation files directly from the web.

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📜 License

Apache 2.0 © 2025 Vihaan Sahu

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Example / Result

Here’s an example traffic-light plot generated using Critiplot with different themes:

NOS

GRADE

ROBIS

JBI Case Report

JBI Case Series