Extracting Structured Data from Research Papers — What to Define Before You Upload

If you have ever tried to pull study data out of fifty PDFs by hand, you already know the problem. The information is there — sample size, intervention, primary endpoint, key finding — but it's spread across abstracts, methods sections, and tables that look different in every paper.

The instinct, when you switch to an AI tool, is to upload the stack and start typing the fields you want. That almost always produces a mess on the first run. Not because the model is bad, but because you haven't decided what you actually want yet.

This post is about the five things worth deciding before you upload. None of them are technical. They take about ten minutes. They are the difference between a usable evidence table and a spreadsheet you have to redo from scratch.

1. Decide what counts as one row

Before anything else: is one PDF one row, or one PDF many rows?

A literature review usually wants one row per paper. Title, authors, design, finding, page reference. Done.

But sometimes you want one row per something inside the paper — one row per cohort, one row per reported outcome, one row per study arm. A meta-analysis often looks like this. The same paper produces three rows because it reports three subgroup results.

These are very different jobs. Decide which one you're doing first. If you're doing the second one, write down the entity name (cohort, arm, outcome) and what makes two of them different in the same paper. That sentence becomes your row definition.

If you can't write that sentence in plain English, you don't yet know what you want, and the tool can't either.

2. Pick the smallest set of fields you can verify

The temptation is to define every field you might ever want — fifteen columns, twenty columns. Don't.

Pick the fields you can verify against a paper in under thirty seconds. For most reviews that's three to six fields. Sample size. Population. Primary outcome. Effect size. Maybe one more.

The reason is brutal: every field you add multiplies the time you spend reviewing results. If a field is hard to verify (because you have to read three pages to confirm it), you'll skip the verification, and an unverified field is just noise wearing a column header.

You can always add more fields once the first run looks right. Going small first and expanding is cheap. Going wide first and pruning is expensive.

3. Write down what "not found" looks like

Research papers vary. Some report sample size in the abstract. Some bury it in a methods table. Some don't report it cleanly at all because the study used a rolling enrollment.

For every field you define, decide what should happen when the value isn't there. Three honest options:

• Return "Not found" — the safest default. Easy to spot in review.
• Return your best inference — risky. You'll forget which values were inferred and which were quoted.
• Skip the row — only useful when a field is mandatory for the comparison you're doing.

If you don't decide this up front, the model picks for you, and the picks are inconsistent across papers. That inconsistency is what makes evidence tables feel unreliable. It's not the wrong values that hurt — it's not knowing whether a blank cell means "not in the paper" or "model didn't try hard enough."

4. Pin one field to a single, narrow definition

Pick the field most likely to be ambiguous, and write a one-line rule for how to interpret it.

For research papers, this is almost always the primary outcome or primary endpoint. Different papers use different language. Some report a primary, secondary, and exploratory. Some have a composite. Some change the primary endpoint between protocol and publication.

A useful rule looks like: "Use the primary endpoint as defined in the methods section. If both pre-specified and post-hoc are reported, return the pre-specified one."

That's it. One sentence. You don't need to anticipate every edge case. You need to remove the most common ambiguity, so the model isn't guessing your preference twenty different ways across twenty papers.

5. Run two papers before you run fifty

This is the one nobody does. Everyone uploads the full batch on the first run.

Pick two papers from your stack that look maximally different — one short report, one dense full-text article. Run those two first. Look at the results. The flaws you'll find in two papers are the same flaws you'd find in fifty, except now you can fix them before you've spent twenty minutes waiting on extraction.

The fixes are usually one of three things: rename a field, tighten a rule, or split a field that was doing two jobs.

What this looks like in DocExtract

When you set up an extraction job, the product asks you these questions in order — what fields, what to do when missing, what rules apply. The instruction pack you approve at the end is a written-out version of every decision you made.

If your evidence table comes out wrong, the instruction pack is where the wrong decision lives. You don't have to guess. Open it, fix the line, run again.

That's the whole loop: decide what you want, run a small batch, check the evidence behind each value, refine the rules, scale up. The model does the reading. Your job is to define the question precisely enough that "reading" is a well-defined task.

Most of what makes a research-paper extraction succeed happens before you upload anything. Spend the ten minutes.

Use DocExtract for public, open-access, or non-confidential research documents only. Do not upload patient records, identifiable personal data, or regulated health information.