AI Meta Analysis Search Strategy
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What are you actually pooling? Before a single database is opened, a meta analysis has to know which effect it intends to combine, measured how, in which populations, against which comparator. Get that wrong and the search returns a pile of studies that cannot be added together. AI Meta Analysis Search Strategy builds the search backwards from the effect you plan to pool.
Short answer: AI Meta Analysis Search Strategy is a free tool that drafts a search plan for a meta analysis, built from your eligibility criteria, comparators, and outcome measures, with an extraction field list and routes to grey literature.
What is AI Meta Analysis Search Strategy?
AI Meta Analysis Search Strategy produces the search planning document that sits in front of a quantitative synthesis. You describe the effect you want to pool, the populations, the comparators, and the outcome measures you can combine. It returns eligibility criteria, search concepts, extraction fields, and a note on where unpublished results are likely to sit.
It differs from a general review search in one important way. A meta analysis is constrained by comparability. A study can be perfectly relevant and still be unusable because it reports a different outcome measure, so the search has to be designed around what can actually be pooled.
It does not run searches or count anything This tool drafts a plan. It has no database access, cannot execute a query, cannot return hit counts, and must never be asked for citations, effect sizes, or study characteristics. Every number in a meta analysis comes from studies you retrieved and extracted yourself. Any reference that appears in a draft should be treated as fabricated and removed.
Why Use AI Meta Analysis Search Strategy?
The decisions that determine whether a synthesis is possible are made before the first search runs.
- Forces eligibility to be written in terms of what can be combined, not just what is relevant.
- Ties outcome measures to the search, so you do not retrieve studies you cannot use.
- Produces an extraction field list at planning stage, which is when it is cheap to change.
- Prompts grey literature and trial registry routes, which is where publication bias hides.
- Gives you a document to register alongside a protocol rather than a plan in your head.
How Does AI Meta Analysis Search Strategy Work?
In the prompt box, state the effect you intend to pool, the population, the intervention or exposure, the comparator, and the outcome measures you can combine. Say which databases and registries you have access to, and any language or date restrictions you intend to apply.
Choose an engine from the model selector below. AI Meta Analysis Search Strategy runs on MSB AI, Anthropic Claude AI, OpenAI ChatGPT, Google Gemini, DeepSeek, Qwen and more. Structured planning benefits from a reasoning heavy engine, and a second run often catches an eligibility criterion the first one left implicit.
Choose your options in the advanced options accordion, then generate the plan. The output card shows the plan with a live word count, which helps when the strategy has to fit a protocol template or a registration form. Every plan carries Copy, Listen, Reuse and Download, and can be exported as DOC, TXT or HTML. DOC is the practical export, since this document goes to a supervisor, a librarian, and a registry. Reuse lets you expand the extraction fields into a full data collection form. The activity history panel keeps every version from this session.
| What you put in the prompt | What changes in the plan |
|---|---|
| The exact outcome measures you can pool | Eligibility excludes studies you could not combine |
| The comparator you require | The comparison concept enters the search properly |
| Your access to registries | Grey literature routes become realistic rather than aspirational |
| "List extraction fields" | The data collection form is planned before screening starts |
Anatomy Of A Pooling Focused Search Plan
| Element | What it determines | Where plans go wrong |
|---|---|---|
| Eligibility | Which studies can enter the pool | Written for relevance, not comparability |
| Outcome measures | What can actually be combined | Left vague until extraction |
| Comparator | Which contrasts are meaningful | Omitted, producing incomparable arms |
| Grey literature | How much publication bias you can assess | Skipped for time |
Who Builds One Of These
- Doctoral researchers whose thesis includes a quantitative synthesis.
- Clinical and health services researchers working to established reporting standards.
- Evidence synthesis teams preparing a protocol for registration.
- Social scientists and educationalists pooling effect sizes across heterogeneous designs.
- Anyone updating an existing meta analysis, where the original strategy must be reproduced and extended.
Setting Depth, Angle, And Rigor
Every control below is in the advanced options accordion on the AI Meta Analysis Search Strategy page.
| Option | What it controls | When to change it | Suggested starting point |
|---|---|---|---|
| Depth | How thorough the plan is | Comprehensive for a registered protocol | Deep, since planning detail pays for itself here |
| Angle | The analytical frame used | Cost-Benefit when scoping feasibility against time | Use-Case Fit, which keeps eligibility tied to pooling |
| Output Format | The layout of the result | Table for eligibility and extraction fields | Structured Sections |
| Audience Level | Who the plan assumes it addresses | Expert for a methods supervisor or a librarian | Expert |
| Include Data Points | Adds figures to the plan | Off; no counts or effect sizes may be generated | Off |
| Include Recommendations | Adds suggested choices | On for scoping, off once the protocol is fixed | On during planning |
| Include Risks / Caveats | Adds threats to validity | Keep on, since heterogeneity and bias belong here | On |
| Include Next Steps | Adds the actions that follow | On, since registration and librarian review come next | On |
| Analytical Rigor | How formal the reasoning is | Higher for a registered protocol | Around 75 |
| Custom Instructions | Free text for anything the menus miss | State the effect, comparator, and poolable outcomes | "Eligibility by comparability, list extraction fields, include registry and grey literature routes, no citations" |
Key Features
Built from the pooled effect
Eligibility is written around what can actually be combined, not around what sounds relevant.
Extraction fields early
The data collection form is drafted at planning stage, when changing it is still free.
Grey literature routes
Registries, theses, conference proceedings, and unpublished results get their own section.
Bias considered upfront
Heterogeneity and publication bias are treated as search stage problems, which is where they are decided.
Protocol ready
Export to DOC for a supervisor, a librarian, and the registration record.
When To Write It
Write the strategy before you register the protocol and before you run any search. A meta analysis whose eligibility criteria are settled after the results are visible is no longer a meta analysis in any meaningful sense, and reviewers ask about the ordering.
Register the protocol once the strategy is stable and a librarian has reviewed it. Deviations after registration are permitted and normal, but they must be recorded and explained, which is much easier when the original plan is a document rather than a memory.
Heterogeneity is decided at the search stage Whether the studies you retrieve can be sensibly pooled is determined by the eligibility criteria you write now, not by the statistical model you fit later. Broad criteria produce a large pool and an uninterpretable summary estimate. Decide how much clinical and methodological variation you are willing to accept, write it down, and let the search enforce it.
Get a librarian to peer review the strategy Information specialists routinely find missing synonyms, wrong field tags, and database specific errors that cost weeks of screening. Most institutions offer this, and most researchers never ask.
Before You Register Checklist
- ✅ Eligibility criteria are written in terms of what can be pooled.
- ✅ The outcome measures you intend to combine are named specifically.
- ✅ The comparator is stated, not assumed.
- ✅ Extraction fields are listed before screening begins.
- ✅ Grey literature and registry routes are included.
- ✅ No citation, count, or effect size in the document came from the tool.
- ✅ A librarian or information specialist has reviewed the strategy.
- ✅ The protocol is registered before the searches run.
Run a scoping search first A quick exploratory search before you fix the criteria tells you whether enough comparable studies exist. Finding that out now is far better than finding it out after screening two thousand records.
Pros And Cons
What works well
- Ties eligibility to comparability, which is the distinctive requirement here.
- Extraction fields drafted at planning stage save substantial rework.
- Grey literature routes are prompted rather than forgotten.
- Free with no account, and usable through several protocol drafts.
What to watch
- It cannot run a search, count hits, or verify that studies exist.
- Any citation or figure it produces should be treated as invented.
- It does not know your field's conventions for outcome measures.
- Statistical decisions about pooling belong in the protocol and to a statistician.
AIToolsay is a free set of AI tools for research and writing, open and free throughout. Evidence synthesis usually needs more than one plan, and the AI Systematic Review Search Strategy covers the reproducible database by database version, while the AI Statistical Method Explainer helps when the pooling method itself has to be explained to a reader. You can run AI Meta Analysis Search Strategy through as many protocol drafts as you need, at no cost.
Frequently Asked Questions
Can it find the studies for me?
No. It has no database access and cannot retrieve or verify a single reference. It plans the search; you run it.
Is the AI Meta Analysis Search Strategy free to use?
Yes. AI Meta Analysis Search Strategy requires no account and costs nothing, and you choose which AI model drafts the plan.
How is this different from a systematic review search?
A systematic review search is built for reproducible retrieval. A meta analysis search is additionally constrained by comparability, because studies must be poolable, not merely relevant.
Should I include unpublished studies?
Wherever you can. Registries, theses, and conference abstracts are how you assess publication bias, and excluding them by default weakens the synthesis.
Do I have to register the protocol?
In many fields it is expected, and in health research it is close to mandatory for publication. Register before searching, and record any later deviation.
What if the studies use different outcome measures?
Decide at planning stage which measures you can convert or combine, and exclude the rest explicitly. Deciding this after extraction invites a great deal of doubt about the result.
Does it replace an information specialist?
No. A librarian's peer review of a search strategy catches errors that cost weeks, and it is the single highest value review in the whole process.
A meta analysis lives or dies on decisions made before the first database is opened. Define the effect, write eligibility around comparability, plan your extraction, and get the strategy reviewed. Thank you for reading, and good luck with the synthesis.
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