Critiplot: A Critical Appraisal Plot Visualiser for Risk of Bias in Systematic Reviews and Meta-Analyses
Bibliographic record
Abstract
Critiplot is an open-source, web-based application designed for generating high-quality visualizations of risk-of-bias and critical appraisal assessments in systematic reviews, meta-analyses, and other evidence synthesis workflows. It supports multiple widely-used critical appraisal frameworks, including NOS (Newcastle–Ottawa Scale), GRADE, ROBIS, and JBI tools (for case reports and case series), providing researchers with flexible, reproducible, and publication-ready tools for study evaluation. Critiplot enables the creation of traffic light plots for individual study-level assessments and weighted bar plotssummarizing domain-level judgments across multiple studies, facilitating rapid interpretation of complex quality data. The platform also offers pre-structured CSV and Excel templates for standardized data input and reproducibility, while customizable visualization themes help create figures suitable for manuscripts, presentations, reports, or supplementary materials. Designed with methodological rigor in mind, Critiplot promotes transparent workflows and open-source accessibility. By combining flexibility, ease of use, and high-quality visual outputs, it helps researchers communicate study quality clearly, improve transparency in evidence synthesis, and support informed decisions in healthcare and scientific research.
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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 | 0.144 | 0.304 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.025 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.152 | 0.018 |
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".