Imprecision and inconsistency are two of the eight GRADE domains, and two of the five domains that can lead to rating down of the certainty of the evidence. Evaluating imprecision involves considering whether confidence intervals around absolute estimates, such as risk differences or mean differences, cross predetermined thresholds. Evaluating inconsistency involves considering whether there are systematic differences across the study results included in evidence synthesis.
Session 6 of the GRADE Learning Live series covered how to rate imprecision and inconsistency in systematic reviews of interventions. The domains are related, so were considered together in the session.
Topics included:-
- What is imprecision? What is inconsistency?
- How to evaluate imprecision and inconsistency in reviews of interventions
- Impact of imprecision and inconsistency on the certainty of evidence
- Additional considerations when rating imprecision (large effects, low baseline risk) and inconsistency (whether inconsistency can be explained)
- Overlap of the domains of imprecision and inconsistency
The webinar was suitable for those wanting to use GRADE to interpret and summarise findings in a systematic review. An understanding of systematic review methods and content covered in the introductory session is assumed.
The session was delivered in August 2026 and below you will find the recording with the accompanying slides to download [PDF].
Presenter Bio
Ignacio Neumann is a physician and guideline methodologist with extensive experience in evidence synthesis and the development of clinical practice guidelines. He is an active member of the GRADE Working Group, where he contributes to methodological advances in assessing certainty of evidence, defining decision thresholds, and strengthening the integration of evidence into recommendations. He is also actively involved in the development of digital and AI-enabled tools designed to support structured evidence translation. His work focuses on preserving methodological rigor while enhancing accessibility, transparency, and the scalability of evidence-based recommendations across clinical and public health settings.
