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176 lines
4.6 KiB
Markdown
176 lines
4.6 KiB
Markdown
---
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name: pubmed-database
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description: Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.
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origin: community
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---
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# PubMed Database
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Use this skill when a task needs biomedical literature from PubMed rather than
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general web search.
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## When to Use
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- Searching MEDLINE or life-sciences literature.
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- Building PubMed queries with MeSH terms, field tags, dates, or article types.
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- Looking up PMIDs, abstracts, publication metadata, or related citations.
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- Running systematic-review search passes that need repeatable search strings.
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- Using NCBI E-utilities directly from Python, shell, or another HTTP client.
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## Query Construction
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Start with the research question, split it into concepts, then combine concepts
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with Boolean operators.
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```text
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concept_1 AND concept_2 AND filter
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synonym_a OR synonym_b
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NOT exclusion_term
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```
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Useful PubMed field tags:
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- `[ti]`: title
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- `[ab]`: abstract
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- `[tiab]`: title or abstract
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- `[au]`: author
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- `[ta]`: journal title abbreviation
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- `[mh]`: MeSH term
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- `[majr]`: major MeSH topic
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- `[pt]`: publication type
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- `[dp]`: date of publication
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- `[la]`: language
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Examples:
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```text
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diabetes mellitus[mh] AND treatment[tiab] AND systematic review[pt] AND 2023:2026[dp]
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(metformin[nm] OR insulin[nm]) AND diabetes mellitus, type 2[mh] AND randomized controlled trial[pt]
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smith ja[au] AND cancer[tiab] AND 2026[dp] AND english[la]
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```
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## MeSH and Subheadings
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Prefer MeSH when the concept has a stable controlled-vocabulary term. Combine
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MeSH with title/abstract terms when the topic is new or terminology varies.
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Correct subheading syntax puts the subheading before the field tag:
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```text
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diabetes mellitus, type 2/drug therapy[mh]
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cardiovascular diseases/prevention & control[mh]
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```
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Use `[majr]` only when the topic must be central to the paper. It can improve
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precision but may miss relevant work.
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## Filters
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Publication types:
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- `clinical trial[pt]`
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- `meta-analysis[pt]`
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- `randomized controlled trial[pt]`
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- `review[pt]`
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- `systematic review[pt]`
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- `guideline[pt]`
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Date filters:
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```text
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2026[dp]
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2020:2026[dp]
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2026/03/15[dp]
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```
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Availability filters:
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```text
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free full text[sb]
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hasabstract[text]
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```
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## E-utilities Workflow
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NCBI E-utilities supports repeatable API workflows:
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1. `esearch.fcgi`: search and return PMIDs.
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2. `esummary.fcgi`: return lightweight article metadata.
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3. `efetch.fcgi`: fetch abstracts or full records in XML, MEDLINE, or text.
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4. `elink.fcgi`: find related articles and linked resources.
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Use an email and API key for production scripts. Store API keys in environment
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variables, never in committed files or command history.
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```python
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import os
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import time
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import requests
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BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils"
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def esearch(query: str, retmax: int = 20) -> list[str]:
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params = {
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"db": "pubmed",
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"term": query,
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"retmode": "json",
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"retmax": retmax,
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"tool": "ecc-pubmed-search",
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"email": os.environ.get("NCBI_EMAIL", ""),
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}
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api_key = os.environ.get("NCBI_API_KEY")
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if api_key:
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params["api_key"] = api_key
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response = requests.get(f"{BASE}/esearch.fcgi", params=params, timeout=30)
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response.raise_for_status()
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time.sleep(0.35)
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return response.json()["esearchresult"]["idlist"]
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pmids = esearch("hypertension[mh] AND randomized controlled trial[pt] AND 2024:2026[dp]")
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print(pmids)
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```
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For batches, prefer NCBI history server parameters (`usehistory=y`,
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`WebEnv`, `query_key`) instead of passing very long PMID lists through URLs.
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## Output Discipline
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For each search pass, record:
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- exact search string
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- database searched
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- date searched
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- filters used
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- result count
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- export format
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- any manual exclusions
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Example:
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```markdown
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| Database | Date searched | Query | Filters | Results |
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| --- | --- | --- | --- | ---: |
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| PubMed | 2026-05-11 | `sickle cell disease[mh] AND CRISPR[tiab]` | 2020:2026[dp], English | 42 |
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```
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## Review Checklist
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- Are field tags valid PubMed tags?
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- Are MeSH terms paired with free-text synonyms for newer topics?
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- Is the date range explicit and appropriate?
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- Does the search log include enough detail to reproduce the query?
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- Are API keys loaded from the environment?
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- Does HTTP code call `raise_for_status()` or otherwise handle non-200
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responses before parsing?
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- Are rate limits respected?
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## References
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- [PubMed help](https://pubmed.ncbi.nlm.nih.gov/help/)
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- [NCBI E-utilities documentation](https://www.ncbi.nlm.nih.gov/books/NBK25501/)
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- [NCBI API key guidance](https://support.nlm.nih.gov/kbArticle/?pn=KA-05317)
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- NCBI support: <eutilities@ncbi.nlm.nih.gov>
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