CONSORT Diagram
Chart overview
CONSORT diagrams are standardized flow charts required by journals for reporting randomized controlled trials, showing the number of participants screened, enrolled, randomly allocated, followed up, and analyzed at each stage.
Key points
- Clinical researchers use these figures to transparently document exclusions, dropouts, and protocol deviations according to CONSORT 2010 guidelines.
- The diagram structure ensures reproducibility and allows readers to assess potential bias in participant selection and attrition.
Example Visualization
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Generate publication-ready consort diagrams with AI in seconds. No coding required – just describe your data and let AI do the work.
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"Create a CONSORT flow diagram from my data. Draw a vertical flowchart with boxes for Enrollment (screened, excluded with reasons, enrolled), Allocation (intervention and control arms), Follow-up (lost to follow-up per arm), and Analysis (analyzed per arm). Connect boxes with downward arrows. Use clean rectangular boxes with black borders, Arial font, and journal formatting."
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Python Code Example
Console Output
Figure saved: plotivy-consort-diagram.png
Common Use Cases
- 1Reporting participant flow in phase II and III randomized controlled trials
- 2Documenting screening, exclusion, and randomization counts in systematic reviews
- 3Visualizing per-protocol versus intention-to-treat analysis populations
- 4Presenting patient attrition and loss to follow-up in longitudinal observational studies
Pro Tips
Use matplotlib FancyBboxPatch and annotations with arrowprops to construct boxes and arrows programmatically
Strictly follow CONSORT 2010 box labels and stage names to satisfy journal requirements
Include reason counts for all exclusions in nested format within each exclusion box
Maintain consistent box widths and vertical spacing to keep the diagram readable at journal column width
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.