A curated collection · 5 starting points
Separate the record of what your study did from the model of what you believe may cause an outcome. Review-selection flows, trial participant flows and causal diagrams answer different questions. Start with the appropriate structure and replace all illustrative counts and labels with your own evidence.
Choose a templateRead the example and its source before opening it in the editor. Each card identifies the workstation and the plan needed to edit there.
PRISMA 2020 flow
Choose this for a systematic review whose identification, screening, retrieval and eligibility counts need to reconcile. It distinguishes included reports from studies and supplies named full-text exclusion reasons. The example counts are teaching data.
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PRISMA 2020 flow
Choose this for an update that carries reports forward from an earlier review. Keep those previous reports separate from records screened in the current search rather than forcing the new search's counts to account for them.
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PRISMA 2020 flow
Choose this when records from databases and registers follow a different selection path from reports identified through other methods. Preserve the separate arm only when that is how the review actually handled those reports.
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Mermaid
Choose this as an editable participant-flow illustration for a randomised trial. Replace enrolment, allocation, follow-up and analysis counts from the trial record, and check the reporting guidance applicable to your study. This Mermaid template is not a compliance validator.
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Graphviz (DOT)
Choose this to make causal assumptions explicit before discussing adjustment or interpretation. A DAG describes an assumed causal structure; drawing an arrow does not establish a causal effect, and it does not replace a participant or record-selection flow.
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A closer look
Choose this for a systematic review whose identification, screening, retrieval and eligibility counts need to reconcile. It distinguishes included reports from studies and supplies named full-text exclusion reasons. The example counts are teaching data.
Explore this templateRecords, reports, studies and participants are different quantities. Keep the unit visible, preserve a separate account of exclusions, and trace the numbers back to the search, screening or trial log. Do not change a number simply to make a figure balance.
The PRISMA engine checks the source you enter for arithmetic, missing terms and other reporting issues. It cannot establish search completeness, eligibility decisions or the validity of a study. Review the figure against the applicable reporting standard and retain the underlying evidence.
Reading the public examples and source is free. Editing requires an account with access to the named workstation. Free includes the Studio with up to 3 saved figures and 3 cloud Library projects; dedicated workstations such as Weave and BPMN start on Starter.
Replace example labels, counts and policies before using a template for your work. Optional AI features and exports have their own plan and usage limits.
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