GRADE Evidence Assessment Support
GRADE support for rating certainty of evidence across outcomes and preparing transparent evidence profiles.
GRADE rates certainty of a body of evidence for a specific outcome, not the quality of a single paper. Tezyar supports structured consideration of risk of bias, inconsistency, indirectness, imprecision, publication bias, and relevant upgrading domains where applicable.
Outcome-Specific Certainty
Certainty can differ across outcomes in the same review, so each critical outcome needs its own reasoning.
Downgrading Domains
Risk of bias, inconsistency, indirectness, imprecision, and publication bias are considered explicitly rather than collapsed into one general quality label.
Transparent Explanations
Every downgrade or upgrade should be justified in concise footnotes or evidence-profile notes so readers can follow the judgment.
Link GRADE to the Review Conclusions
The certainty rating should shape how strongly conclusions are stated and where uncertainty is highlighted.
Frequently Asked Questions
Is GRADE the same as risk-of-bias assessment?
No. Risk of bias is one input into a broader certainty-of-evidence judgment.
Do all systematic reviews require GRADE?
No. Its relevance depends on the review purpose, evidence type, and reporting expectations.
Related specialized topics
Systematic review search strategy support for concepts, databases, controlled vocabulary, Boolean logic, documentation, and reproducibility.
Risk-of-bias assessment support using study-design-appropriate tools such as RoB 2, ROBINS-I, and JBI instruments.
Meta-analysis support in R for effect-size preparation, random- and fixed-effect models, heterogeneity, subgroup analysis, meta-regression, and forest plots.
PRISMA 2020 support for systematic review reporting, flow diagrams, checklists, methods transparency, and manuscript review.
PROSPERO protocol support for eligible systematic reviews: review question, eligibility, outcomes, search approach, bias assessment, and synthesis plan.
Network meta-analysis support for multi-treatment evidence networks, assumptions, modelling, inconsistency assessment, ranking, and reporting.