Full-text screening checklist for literature reviews

Full-text screening is where a literature review becomes more serious. Title and abstract screening can remove obvious mismatches, but full-text screening decides whether a paper actually meets the review criteria.

This stage often reveals problems that abstracts hide: wrong population, missing outcomes, weak methods, inaccessible data, duplicate reports, or claims that do not match the review question.

This guide provides a full-text screening checklist for literature reviews and explains how to make full-text decisions clear enough to report later.

Full-text screening is the process of reading the full paper or report to decide whether it should be included in the review. It comes after title and abstract screening.

During full-text screening, reviewers check:

• Whether the source meets inclusion criteria.
• Whether exclusion criteria apply.
• Whether required details are present.
• Whether the study design fits.
• Whether outcomes or concepts match the question.
• Whether the paper should move to data extraction.

The decision should be based on the full source, not just the abstract.

Full-text screening is necessary because titles and abstracts often do not contain enough information. A paper can look relevant in the abstract but fail once the methods, sample, or outcomes are inspected.

Full text can reveal:

• The wrong study design.
• A different population than expected.
• No usable outcome data.
• Missing methodological detail.
• A focus outside the review scope.
• Duplicate publication.
• A paper that discusses the topic but does not study it.

This stage protects the final evidence set from look-alike sources.

For earlier screening stages, see title and abstract screening vs full-text screening.

Before full-text screening begins, make sure the screening criteria are ready. Full-text review becomes messy when reviewers invent rules as they go.

Prepare:

• Inclusion criteria.
• Exclusion criteria.
• Full-text exclusion reason categories.
• Reviewer roles.
• Conflict resolution rules.
• Full-text retrieval process.
• A recordkeeping format.
• A plan for unavailable full texts.

If AI helped prioritize records, document that before full-text screening begins.

For AI-assisted screening records, see PRISMA flow diagram with AI-assisted screening.

A full-text screening checklist should translate the review criteria into questions reviewers can answer consistently.

Include questions such as:

• Is the full text available?
• Is this the correct publication type?
• Does the population or source material match?
• Does the method fit the review?
• Does the paper measure or discuss the required outcome?
• Does the time period fit?
• Is the setting relevant?
• Is the paper a duplicate report?
• Is the article corrected, retracted, or flagged?
• Should the paper proceed to extraction?

Each question should connect to an inclusion or exclusion rule.

Population or sample fit is one of the most common full-text exclusion reasons. Abstracts often describe the topic broadly, while the full paper reveals a narrower or different group.

Check:

• Who or what was studied?
• How were participants, cases, texts, records, or datasets selected?
• Are subgroups relevant?
• Does the sample match the review question?
• Are inclusion criteria inside the paper compatible with your review?
• Is the sample only mentioned in passing?

If the population does not fit, record the exclusion reason clearly. Do not keep a paper because it is generally interesting.

Method fit means the paper uses a design that can answer the review question. A paper can discuss your topic but still use a method outside scope.

Check:

• Is it empirical, theoretical, methodological, or commentary?
• Does the design match the review protocol?
• Are methods described clearly?
• Is the analysis relevant to your question?
• Does the paper provide usable evidence?
• Is the paper only a background discussion?

If your review compares methods, record why the method belongs or does not belong.

For method comparison, see how to compare methods across research papers.

Outcome or concept fit decides whether the paper actually reports the evidence your review needs. This is where many abstract-level candidates fail.

Check:

• Is the outcome measured or only mentioned?
• Is the concept defined clearly?
• Does the paper report results relevant to your question?
• Are measures comparable with other included studies?
• Does the paper address your review's main concept or a side issue?
• Are results usable for synthesis?

If the outcome is missing, do not force the paper into the review because the topic looks close.

Exclusion reasons should be short, consistent, and specific. They should explain why the paper failed at full text.

Good reasons include:

• Wrong population.
• Wrong outcome.
• Wrong study design.
• Not peer reviewed, if required.
• Full text unavailable.
• Duplicate report.
• Not primary research, if primary studies are required.
• Outside date range.
• Retracted source.

Avoid vague reasons like "not relevant" when a more specific category applies. Specific reasons help later reporting and reduce reviewer disagreement.

AI can help locate relevant sections, summarize methods, flag possible exclusion reasons, and compare papers against criteria. It should not make final full-text decisions without human review.

Use AI to ask:

• Where does the paper describe the sample?
• What method does the paper use?
• Which outcomes are reported?
• Does the paper state limitations?
• What information is missing?
• Which exclusion criterion might apply?

Then verify the answer in the source. AI assistance is most useful when it points reviewers to the right place in the full text.

For responsible use, see responsible AI automation checklist for research teams.

Turn the advice into a repeatable workflow by defining the decision you need to make, the evidence required for that decision, and the record that will prove how the decision was made. In screening, the problem is rarely one missing tool. The problem is usually that search, reading, checking, and writing happen in separate places without a shared rule.

Use a short operating routine:

• Name the review question or subquestion.
• Define the source set you are working from.
• Decide what counts as enough evidence for the next step.
• Apply the same criteria to every paper in that step.
• Mark uncertain cases instead of forcing a clean answer.
• Keep source locations for claims that may enter the final review.
• Review the workflow after each major search, screening, or writing session.

This routine keeps the work moving without making the review careless. It also gives supervisors, collaborators, and future you a way to understand why the source set changed.

Record the pieces that would be hard to reconstruct later. You do not need a diary of every click, but you do need enough detail to explain the path from question to source to claim.

For this topic, the most useful record usually includes criteria, reviewer decisions, exclusion reasons, audit checks, and source flow. Add the date, tool or source used, reviewer status, and next action. If AI assisted the step, write down what it helped with and what a human checked.

The record should distinguish discovery from evidence. A tool may help find a paper, but the paper itself must support the claim. A summary may help triage a source, but the original source should support any statement that appears in the literature review.

Before writing, check whether the workflow has produced usable evidence or only useful notes. Notes help you think. Evidence supports a sentence.

Ask:

• Which claim will this source support?
• Is the claim narrower than the evidence?
• Have methods, sample, outcome, or concept details been checked?
• Are limitations visible?
• Are conflicting papers handled rather than ignored?
• Is the citation real, current, and relevant?
• Can another reader understand how this source entered the review?

If the answer is unclear, keep the point in notes rather than moving it into the draft. This is the small pause that prevents AI-assisted research from becoming polished but weak writing.

WisPaper can help researchers build the paper set that enters full-text screening. Deep Search, Scholar Agent, and Inspiration Discovery support natural-language academic search, while paper cards show source labels, summaries, authors, publication details, and preview images for triage.

Once candidate papers are saved or uploaded into My Library, researchers can keep the source set in one place. Library QA can answer questions based on the user's own library, which may help reviewers inspect whether papers address the needed method, population, or outcome before final screening decisions.

WisPaper supports discovery, organization, and paper questions. The final full-text screening decision should remain tied to the review criteria and verified against the paper.

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