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PRISMA 2020 Checklist: A Practical Guide for Systematic Review Authors

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The PRISMA 2020 checklist (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is a 27-item evidence-based reporting standard designed to ensure transparency across literature searches, study selection, risk-of-bias evaluation, and statistical synthesis.

Reporting Guideline Context: PRISMA governs systematic reviews and meta-analyses. If you are conducting a clinical trial (CONSORT), observational study (STROBE), or animal experiment (ARRIVE), use our Research Reporting Guidelines Checklist and Finder to identify the appropriate standard.

What Changed in the PRISMA 2020 Statement?

The updated PRISMA 2020 framework reflects contemporary systematic review practices, including automated machine learning screeners, pre-registration registries (such as PROSPERO), open science data sharing, and updated risk-of-bias tools (like RoB 2 and ROBINS-I).

PRISMA is not a quality appraisal instrument for the underlying studies; it is an author transparency tool ensuring reviewers provide enough procedural detail for independent replication.

The 27 Checklist Items Across Core Sections

To satisfy peer review requirements in high-impact medical and scientific journals, structure your manuscript across PRISMA’s 7 primary reporting domains:

Section Checklist Items Essential Reporting Requirements
Title Item 1 Identify the report explicitly as a systematic review, meta-analysis, or both.
Abstract Item 2 Follow the structured PRISMA 2020 for Abstracts (background, objectives, eligibility, sources, risk of bias, synthesis, registration).
Introduction Items 3–4 Describe the scientific rationale and explicitly define review objectives using the PICO framework (Population, Intervention, Comparator, Outcome).
Methods Items 5–15 Report databases, full reproducible search strings, screening criteria, data extraction tools, risk-of-bias assessment methods, and statistical synthesis models. Read How to Write the Methods Section.
Results Items 16–22 Document search results, study characteristics, individual study risk of bias, synthesized outcomes, and sensitivity analyses. Read How to Write the Results Section.
Discussion Item 23 Provide evidence interpretation, discuss limitations of the included evidence and review process, and state real-world implications. Read How to Write the Discussion Section.
Other Info Items 24–27 State protocol registration details (e.g., PROSPERO ID), funding sources, competing interests, and open data/code availability.

The 4 Stages of the PRISMA 2020 Flow Diagram

A central requirement for systematic reviews is the standardized PRISMA flow diagram tracking the lifecycle of identified records:

  1. Identification: Total records identified across databases (PubMed, Scopus, Web of Science, Embase), grey literature, and citation registers, alongside duplicates removed.
  2. Screening: Records screened at title/abstract level and the exact count of records excluded based on pre-defined criteria.
  3. Eligibility: Full-text articles retrieved and evaluated, detailing the explicit reason for exclusion for each individual paper.
  4. Included: Final count of studies included in qualitative synthesis and quantitative meta-analysis.

Common PRISMA Reporting Errors That Cause Rejection

  • Omitting Reproducible Search Strategies: Reporting keywords without providing the exact boolean search strings used in at least one major database (typically placed in the Supplementary Materials).
  • Flow Diagram Arithmetic Mismatches: The number of records in the flow diagram not reconciling with figures in the Results section.
  • Missing Protocol Registration: Failing to report whether a formal protocol was registered on PROSPERO, OSF, or Cochrane prior to screening.
  • Unexplained Discrepancies: Not specifying whether screening and data extraction were performed by two independent reviewers to prevent bias.

Systematic Review Author Audit Checklist

  • The manuscript title contains “Systematic Review” and/or “Meta-Analysis”.
  • Protocol registration number (e.g., PROSPERO) is cited in the Abstract and Methods.
  • At least one comprehensive, line-by-line database search strategy is included in full.
  • Independent double-screening and data extraction procedures are documented.
  • The validated PRISMA 2020 Flow Diagram is generated and arithmetic matches the text.
  • Validated risk-of-bias instruments (RoB 2, ROBINS-I, Newcastle-Ottawa) are reported.
  • Data sharing, code availability, and funding declarations are transparently stated.

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Reviewed by Umair Abbasi

AI Engineer & Data Scientist | PhD Candidate, Artificial Intelligence

Umair Abbasi is an AI Engineer and Data Scientist with a background in machine learning, AI agents, and MLOps, currently pursuing a PhD in Artificial Intelligence and holding a completed M.S. in Data Science. He leads the data architecture and tooling behind the journal metrics database and comparison tools. As an active researcher navigating journal selection and citation metrics in his own academic work, he brings a practitioner's perspective to how impact factor, CiteScore, and related metrics are actually used — not just how they're defined.

Credentials & Expertise:
- M.S., Data Science (completed) - PhD Candidate, Artificial Intelligence (in progress) - Machine Learning Specialization — Stanford University (Coursera) - Google Data Analytics Professional Certificate

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