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Systematic Review Assistant

Guides systematic literature reviews following PRISMA guidelines with screening, quality assessment, and data extraction frameworks.

A custom GPT by @sysreviewer for research & analysis tasks. Available in the ChatGPT GPT Store with a Plus, Team, or Enterprise subscription.

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Systematic Review Assistant is a custom GPT built by @sysreviewer for guides systematic literature reviews following prisma guidelines with screening, quality assessment, and data extraction frameworks. It is available in the ChatGPT GPT Store under the Research & Analysis category and requires a ChatGPT Plus subscription to access.

About this GPT

Systematic Review Assistant is part of the Research & Analysis category in OpenAI's GPT Store. Custom GPTs are specialized versions of ChatGPT that have been configured with specific instructions, knowledge bases, and capabilities by their creators. This GPT was designed by @sysreviewer to help users with guides systematic literature reviews following prisma guidelines with screening, quality assessment, and data extraction frameworks.

Unlike prompting a general-purpose ChatGPT, this GPT comes pre-configured with the context, tone, and expertise needed for research & analysis-related tasks. This means you spend less time explaining what you need and more time getting useful results.

To use this GPT, you need an active ChatGPT Plus ($20/month), Team, or Enterprise subscription. Once subscribed, you can find it by searching for "Systematic Review Assistant" in the GPT Store or browsing the Research & Analysis category.

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Research & AnalysisBy @sysreviewerChatGPT GPT Store

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FAQ

Common questions about Systematic Review Assistant and how to use it effectively.

01

How does this GPT implement the PRISMA guidelines?

It walks you through each PRISMA 2020 checklist item: formulating the research question (typically using PICO/PICOS framework), defining eligibility criteria, specifying information sources, presenting the search strategy, explaining the selection process, describing data collection and extraction methods, assessing risk of bias, synthesizing results, and addressing limitations. At each step, it provides templates and examples, and it will generate a PRISMA flow diagram description you can use to create the actual figure.

02

Can it do the actual screening of papers?

It can assist with title/abstract screening by applying your inclusion/exclusion criteria to batches of paper metadata and explaining its decisions. However, for any published systematic review, you need at least two independent human screeners and a conflict resolution process. The GPT is best used as a third screener or a quality check — flagging papers where it disagrees with the human screeners so you can re-examine borderline cases. It should not be the sole screener.

03

How does quality assessment work with this tool?

It can guide you through applying quality assessment tools appropriate to your study types: RoB 2 or ROBINS-I for randomized/non-randomized trials, AMSTAR 2 for systematic reviews, CASP checklists for qualitative studies, QUADAS-2 for diagnostic accuracy studies, and others. It will help you apply each domain of the assessment tool to individual papers and generate summary tables. The critical nuance is that it helps you apply the tools consistently, but the quality judgments are yours to validate.

04

What extraction framework does it provide?

It generates customized data extraction forms based on your research question and included study types. A typical form might include: study characteristics (design, sample, setting), intervention/exposure details, outcome measures and results, and quality assessment notes. It can then help you populate these forms from full-text papers and identify where extracted data is inconsistent across papers — a common sign of extraction errors or between-study heterogeneity.

05

How does it handle a very large number of search results?

For searches returning thousands of results, the GPT helps you refine your search strategy to reduce noise before screening begins — suggesting more specific terms, adding exclusion criteria, or narrowing the date range. It can process batches of deduplicated results and maintain a screening log across sessions. However, the context window limits mean you will need to work in batches; it is designed for manageable systematic reviews (up to a few thousand unique records after deduplication), not mega-reviews.

06

Can it help me choose between a meta-analysis and a narrative synthesis?

Yes. It will assess whether your included studies are sufficiently homogeneous in population, intervention, comparator, and outcome to warrant quantitative pooling. If heterogeneity is high (different measurement instruments, different populations, different study designs), it will recommend a narrative or thematic synthesis approach and provide structure for that synthesis. It also warns against forcing a meta-analysis when the studies are too different — a common reviewer criticism.

07

What is the biggest time-saver this GPT provides?

Researchers consistently report that the biggest efficiency gain is in structuring the workflow and producing draft sections of the manuscript. Instead of staring at a blank screen wondering how to describe your search strategy for the fourth review, you get a template populated with your specific details. The screening, extraction, and synthesis still require human judgment, but the surrounding infrastructure — documentation, reporting, formatting — is dramatically accelerated.

08

How do I ensure the review is still publishable if I use AI assistance?

Document everything transparently. In your methods section, describe: which parts of the process used AI assistance, what the GPT did vs. what humans did, and how AI suggestions were validated. Most journals now have AI policies — check the target journal's guidance before submission. The key principle is that the intellectual work of the review (screening decisions, quality judgments, synthesis interpretation, conclusions) must be human-led and verifiable, even if AI accelerated the mechanics.