How Best to Use EvidenceTableBuilder for Systematic Literature Reviews

TL;DR

EvidenceTableBuilder.com works best when you treat it as a structured data extraction tool, not a summariser.

It fits into the selection and data extraction stages of a systematic literature review, anywhere you need to compare studies side-by-side.
Upload up to 20 PDFs at a time and define clear, precise data points — each question becomes one column in your evidence table.
Avoid vague questions. Always ask: “What would I need for analysis later?”
Use single-responsibility questions, specify units, and separate baseline and follow-up values into different questions.
Short, clean PDFs (ideally under ~10 pages) give the most reliable results.
Clear questions in = clean evidence tables out. Vague questions lead to messy tables that are hard to analyse.

Used correctly, EvidenceTableBuilder turns PDFs into a structured table of evidence ready for Excel-based analysis, meta-analysis, and regulatory or HTA workflows.

Evidence synthesis in pharma, HEOR, and market access is rarely about reading papers. It’s about comparing, contrasting, and structuring data so it can be analysed, defended, and reused.

That’s exactly where EvidenceTableBuilder.com fits in.

This guide walks through:

where Evidence Table Builder sits in a standard systematic literature review (SLR),
how to use it effectively as an AI table generator,
and how to avoid the most common mistake we see: asking vague questions.

Where EvidenceTableBuilder Fits in the SLR Workflow

A typical systematic literature review follows a familiar structure:

Define the research question Clearly specify population, intervention, comparator, outcomes, and study design.
Design and run the search strategy Execute reproducible searches across databases such as PubMed, Embase, and Web of Science.
Study selection (screening) Title/abstract screening followed by full-text review to identify eligible studies.
Data extraction and evidence table creation Extract structured data so studies can be compared side-by-side.

EvidenceTableBuilder is designed to support selection and data extraction any point where you need to systematically compare research papers and turn unstructured PDFs into a clean table of evidence.

What EvidenceTableBuilder Actually Does

At its core, EvidenceTableBuilder is an instant data scraper purpose-built for research workflows.

You:

upload your PDFs (up to 20 at a time),
define the exact data points you want to extract,
and let the AI systematically read each paper and place the extracted data into an Excel file.

The output:

Each row = one uploaded study
Each column = one clearly defined data point

This creates a structured AI-generated evidence table that can be used directly for:

early scoping comparisons,
full data extraction for meta-analysis,
regulatory or HTA evidence tables.

How to design evidence tables properly

A Simple Walkthrough

Step 1: Upload your PDFs

Upload up to 20 studies per run. Best results are typically seen with short, clean PDFs (ideally under 10 pages), though longer documents can still work.

Each PDF becomes one row in your final Excel file.

Step 2: Define your extraction questions

This is the most important step.

Each question you write becomes one column in your evidence table.

Think of EvidenceTableBuilder as an AI for research, not a summarisation tool. It performs best when you ask it to extract specific, analysable variables, not general descriptions.

Step 3: Run the extraction

The AI reads each PDF, finds the relevant information, and populates the table.

Step 4: Download your Excel file

You receive a clean, structured evidence table ready for analysis, review, or regulatory use.

How to Write Good Extraction Questions

1. Use single-responsibility questions

Each question should extract one clearly defined variable.

❌ “Extract patient demographics and outcomes.”
✅ “Extract mean age (years) at baseline.”
✅ “Extract percentage of male participants.”

Each variable = one column.

2. Always separate baseline and follow-up

Do not combine timepoints.

❌ “Extract HbA1c at baseline and follow-up.”
✅ “Extract HbA1c (%) at baseline.”
✅ “Extract HbA1c (%) at final follow-up.”

This keeps your table analysable and avoids mixed values.

3. Specify units and context

Never assume units.

❌ “Extract creatinine levels.”
✅ “Extract serum creatinine (µmol/L) at baseline.”

This is especially important for regulatory and HTA tables, where unit consistency matters.

4. Think ahead to analysis

Before running the extraction, imagine opening the Excel file.

Ask yourself:

Can I sort this column?
Can I compare values across studies?
Can I feed this directly into statistical analysis?

If the answer is “not really,” the question needs tightening.

Why Question Quality Matters So Much

EvidenceTableBuilder follows a simple principle:

Junk questions in → junk out.

When questions are vague:

evidence tables become inconsistent,
columns mix multiple concepts,
downstream analysis becomes harder, not easier.

This doesn’t just slow things down it creates risk. Messy evidence tables are difficult to validate, defend, or reuse in regulatory or market access contexts. Working backwards from the analysis stage is best practice.

PDF Preparation: Keep It Simple

You don’t need complex preprocessing.

That said, best performance is typically seen when:

PDFs are text-selectable (not scanned images but it will still work non the less with our computer vision AI),
documents are under ~10 pages,
and each PDF contains a single study.

Splitting very long papers into smaller sections can sometimes improve clarity, but preparation should remain minimal.

Where EvidenceTableBuilder Adds the Most Value

Teams use EvidenceTableBuilder successfully for:

early scoping comparisons across interventions,
structured outcome extraction for meta-analysis,
regulatory and HTA tables of evidence,
internal landscape reviews and gap analyses.

Anywhere structured comparison is required, an AI table generator outperforms manual copy-paste.

Final Thought

EvidenceTableBuilder doesn’t replace methodological thinking. It amplifies it.

When you:

know what data you’ll need later,
ask precise, single-responsibility questions,
and treat the tool as a structured data scraper, not a summariser,

you get clean, analysable evidence tables fast.

Used well, it removes hours (or days) of manual extraction and lets teams focus on interpretation, synthesis, and decision-making the parts that actually require expertise.

That’s where AI for research works best.

Great resources: