Critiplot: A Comprehensive Python Package and Web Tool for Visualizing Risk-of-Bias Assessments in Evidence Synthesis
Bibliographic record
Abstract
Objective: Risk-of-bias (RoB) assessment is a critical component of evidence synthesis, yet visualization of these assessments remains challenging due to the diversity of assessment tools and a lack of standardized visualization approaches. This study aimed to develop Critiplot, a comprehensive solution comprising a Python package and a web tool that supports visualization of multiple RoB assessment frameworks including the Newcastle-Ottawa Scale (NOS), ROBIS, JBI checklists for case reports and case series, and GRADE – a combination not comprehensively supported by any existing single tool. Critiplot is the first tool to generate publication-ready visualizations for each of these frameworks separately, as well as in a unified manner.Methods: Critiplot was developed using Python 3.11, with Streamlit for the web interface, and as a standalone Python package. The tool supports five major RoB assessment tools: Newcastle-Ottawa Scale (NOS), GRADE, ROBIS, JBI Case Report, and JBI Case Series. The tool was evaluated through a technical assessment of its visualization capabilities and a comparative analysis with existing visualization tools. Test datasets were created to represent typical use cases for each assessment tool, including variations in study numbers, RoB distributions, and data formats. Edge case datasets were also created to test the tool's handling of missing data, invalid scores, and inconsistent formatting.Results: Critiplot successfully generates publication-ready traffic light plots and weighted bar plots for all supported assessment tools. The tool offers multiple visualization themes and supports various output formats (PNG, PDF, SVG, EPS). Critiplot is the first tool to generate publication-ready visualizations for NOS, ROBIS, JBI, and GRADE assessments both individually for each framework and in a unified manner within a single platform. The Python package allows for programmatic integration into analysis pipelines, while the web application provides an accessible interface for users without programming experience. Performance evaluation showed that Critiplot efficiently handled datasets of varying sizes, with processing times ranging from 1.5 seconds for 3 studies to 3.8 seconds for 10 studies. The tool demonstrated robustness in handling edge cases, providing clear error messages in 98% of test cases with invalid inputs.Conclusion: Critiplot addresses a significant gap in evidence synthesis methodologies by providing a unified, reproducible approach to RoB visualization across multiple assessment frameworks. Its unique capability to visualize each framework separately (NOS, ROBIS, JBI, and GRADE) alongside unified visualization makes it a pioneering solution. Its open-source design and intuitive interface (both as a package and web tool) make it a valuable addition to the biomedical informatics toolkit, promoting methodological standardization and enhancing the clarity and reproducibility of RoB assessments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.106 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.100 | 0.019 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".