A PubMed search produces citations, not a synthesis. A peptide evidence table makes each record comparable. An evidence table turns each paper into a comparable record so a laboratory team can see which models, endpoints, batches, and methods support a peptide research question. This workflow is designed for transparent educational review, not for copying conclusions from abstracts or making personal-use recommendations.
Short summary: Define one answerable question, make each article a row, choose columns before extraction, record missing information explicitly, compare methods and outcomes, and keep primary results separate from interpretation.
What is a peptide evidence table?
An evidence table is a structured worksheet for a literature review. Each row represents one article, report, or registered study. Columns capture the information needed to compare it with the other rows: peptide identity, model, comparator, endpoint, assay, batch or formulation details, result, limitations, and source link. The table is not a scorecard; it preserves the context that a narrative summary can accidentally flatten.
Define the question before opening PubMed
Write the review question in one sentence and identify the population or model, intervention or material, comparator, outcome, and study type. Add synonyms for the peptide, target, sequence, and assay. Decide in advance whether the table will include cell, animal, human, analytical, or regulatory records. A narrow question produces a table that can be interpreted; a broad keyword dump produces a catalog of unrelated papers.
Choose columns that match the research decision
- Identity: peptide name, synonym, sequence or reference, and article DOI or PMID.
- Model: assay, cell line, species, tissue, participants, or analytical matrix.
- Material context: formulation, batch, concentration range as reported, and sample handling.
- Method: assay platform, controls, calibration, detection limit, and replicate structure.
- Endpoint: the prespecified or measured outcome and how it was quantified.
- Result: the direct observation, effect estimate or descriptive pattern, and uncertainty.
- Limitations: missing details, confounding, model mismatch, and author-stated constraints.
Keep “not reported” distinct from “not applicable.” A blank cell hides uncertainty; an explicit missing-data label makes it visible. The USC literature-review evidence-table guide recommends selecting extraction fields that support comparison rather than collecting every detail that appears in a paper.
Extract one article at a time
Start with the abstract to confirm relevance, then verify each field in the full text, supplement, registry entry, or cited method. Link the row to the original source and record the date accessed. Do not infer a missing endpoint from a nearby figure or convert an author’s proposed mechanism into a measured result. When an article uses several models, split it into subrows or note the model-specific result in the same row.
Compare models, methods, and endpoints
Place studies side by side only after checking whether they actually measure the same thing. Two papers may use the same peptide but different formulations, batches, assay conditions, or endpoint definitions. A cell signaling result is not the same endpoint as a pharmacokinetic observation, and an analytical purity result is not a biological efficacy result.
Use the quality-documentation checklist for identity and analytical fields, the Learn hub for evidence-tier context, and the Research Library for linked primary sources.
Synthesize patterns without hiding disagreement
After extraction, group rows by model, method, endpoint, and material context. Explain whether results are consistent, mixed, or insufficient. A mixed table is not a failed review: it may show that the effect depends on model, formulation, endpoint, or analytical sensitivity. Preserve negative and null findings, and flag studies that are not independently replicated.
Use an update log and reproducible search record
Record the databases, exact query strings, date searched, filters, inclusion decisions, duplicate handling, and reasons for exclusion. Save the table version with a change log. When new evidence arrives, add a row rather than silently replacing an earlier result. This makes the review auditable and helps another researcher reproduce the search.
Research-use-only boundary
This article describes a literature-review method. It does not provide dosing, administration, diagnosis, prevention, treatment, or personal-use instructions. For laboratory research and educational purposes only. Not for human or veterinary use.
Frequently asked questions
How many papers belong in an evidence table?
Include every record that meets the predefined question and eligibility rules. The number should follow the question, not an arbitrary target.
Should abstracts be used for final extraction?
Abstracts are useful for screening, but final fields should be verified in the full text, supplement, registry, or method source whenever possible.
What does “not reported” mean?
It means the reviewed source did not provide the field. It is not evidence that the feature was absent or unimportant.
Can an evidence table prove a mechanism?
No. It organizes and compares evidence. Mechanistic conclusions still depend on the methods, controls, model, and consistency of the underlying studies.
How do I avoid duplicate papers?
Use DOI, PMID, registry ID, and title checks together, then record the reason a duplicate or companion report was merged or retained.
