pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literat…

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技能内容

PubMed Database

Use this skill when a task needs biomedical literature from PubMed rather than

general web search.

When to Use

Searching MEDLINE or life-sciences literature.
Building PubMed queries with MeSH terms, field tags, dates, or article types.
Looking up PMIDs, abstracts, publication metadata, or related citations.
Running systematic-review search passes that need repeatable search strings.
Using NCBI E-utilities directly from Python, shell, or another HTTP client.

Query Construction

Start with the research question, split it into concepts, then combine concepts

with Boolean operators.

concept_1 AND concept_2 AND filter
synonym_a OR synonym_b
NOT exclusion_term

Useful PubMed field tags:

[ti]: title
[ab]: abstract
[tiab]: title or abstract
[au]: author
[ta]: journal title abbreviation
[mh]: MeSH term
[majr]: major MeSH topic
[pt]: publication type
[dp]: date of publication
[la]: language

Examples:

diabetes mellitus[mh] AND treatment[tiab] AND systematic review[pt] AND 2023:2026[dp]
(metformin[nm] OR insulin[nm]) AND diabetes mellitus, type 2[mh] AND randomized controlled trial[pt]
smith ja[au] AND cancer[tiab] AND 2026[dp] AND english[la]

MeSH and Subheadings

Prefer MeSH when the concept has a stable controlled-vocabulary term. Combine

MeSH with title/abstract terms when the topic is new or terminology varies.

Correct subheading syntax puts the subheading before the field tag:

diabetes mellitus, type 2/drug therapy[mh]
cardiovascular diseases/prevention & control[mh]

Use [majr] only when the topic must be central to the paper. It can improve

precision but may miss relevant work.

Filters

Publication types:

clinical trial[pt]
meta-analysis[pt]
randomized controlled trial[pt]
review[pt]
systematic review[pt]
guideline[pt]

Date filters:

2026[dp]
2020:2026[dp]
2026/03/15[dp]

Availability filters:

free full text[sb]
hasabstract[text]

E-utilities Workflow

NCBI E-utilities supports repeatable API workflows:

esearch.fcgi: search and return PMIDs.
esummary.fcgi: return lightweight article metadata.
efetch.fcgi: fetch abstracts or full records in XML, MEDLINE, or text.
elink.fcgi: find related articles and linked resources.

Use an email and API key for production scripts. Store API keys in environment

variables, never in committed files or command history.

import os
import time
import requests

BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils"


def esearch(query: str, retmax: int = 20) -> list[str]:
    params = {
        "db": "pubmed",
        "term": query,
        "retmode": "json",
        "retmax": retmax,
        "tool": "ecc-pubmed-search",
        "email": os.environ.get("NCBI_EMAIL", ""),
    }
    api_key = os.environ.get("NCBI_API_KEY")
    if api_key:
        params["api_key"] = api_key

    response = requests.get(f"{BASE}/esearch.fcgi", params=params, timeout=30)
    response.raise_for_status()
    time.sleep(0.35)
    return response.json()["esearchresult"]["idlist"]


pmids = esearch("hypertension[mh] AND randomized controlled trial[pt] AND 2024:2026[dp]")
print(pmids)

For batches, prefer NCBI history server parameters (usehistory=y,

WebEnv, query_key) instead of passing very long PMID lists through URLs.

Output Discipline

For each search pass, record:

exact search string
database searched
date searched
filters used
result count
export format
any manual exclusions

Example:

| Database | Date searched | Query | Filters | Results |
| --- | --- | --- | --- | ---: |
| PubMed | 2026-05-11 | `sickle cell disease[mh] AND CRISPR[tiab]` | 2020:2026[dp], English | 42 |