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PubMed Database

PubMed数据库

Overview

概述

PubMed is the U.S. National Library of Medicine's comprehensive database providing free access to MEDLINE and life sciences literature. Construct advanced queries with Boolean operators, MeSH terms, and field tags, access data programmatically via E-utilities API for systematic reviews and literature analysis.
PubMed是美国国家医学图书馆的综合数据库，免费提供MEDLINE和生命科学文献的访问权限。可使用布尔运算符、MeSH术语和字段标签构建高级查询，通过E-utilities API以编程方式获取数据，用于系统综述和文献分析。

When to Use This Skill

适用场景

This skill should be used when:
  • Searching for biomedical or life sciences research articles
  • Constructing complex search queries with Boolean operators, field tags, or MeSH terms
  • Conducting systematic literature reviews or meta-analyses
  • Accessing PubMed data programmatically via the E-utilities API
  • Finding articles by specific criteria (author, journal, publication date, article type)
  • Retrieving citation information, abstracts, or full-text articles
  • Working with PMIDs (PubMed IDs) or DOIs
  • Creating automated workflows for literature monitoring or data extraction
本技能适用于以下场景：
  • 检索生物医学或生命科学研究文献
  • 使用布尔运算符、字段标签或MeSH术语构建复杂检索查询
  • 开展系统文献综述或荟萃分析
  • 通过E-utilities API以编程方式访问PubMed数据
  • 根据特定条件（作者、期刊、发表日期、文献类型）查找文献
  • 获取文献引用信息、摘要或全文
  • 处理PMID（PubMed ID）或DOI
  • 构建文献监测或数据提取的自动化工作流

Core Capabilities

核心功能

1. Advanced Search Query Construction

1. 高级检索查询构建

Construct sophisticated PubMed queries using Boolean operators, field tags, and specialized syntax.
Basic Search Strategies:
  • Combine concepts with Boolean operators (AND, OR, NOT)
  • Use field tags to limit searches to specific record parts
  • Employ phrase searching with double quotes for exact matches
  • Apply wildcards for term variations
  • Use proximity searching for terms within specified distances
Example Queries:
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使用布尔运算符、字段标签和专用语法构建复杂的PubMed查询。
基础检索策略：
  • 使用布尔运算符（AND、OR、NOT）组合概念
  • 使用字段标签将检索范围限定到记录的特定部分
  • 使用双引号进行短语精确匹配
  • 使用通配符匹配术语变体
  • 使用邻近检索查找指定距离内的术语
查询示例：
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Recent systematic reviews on diabetes treatment

2023-2024年糖尿病治疗相关系统综述

diabetes mellitus[mh] AND treatment[tiab] AND systematic review[pt] AND 2023:2024[dp]
diabetes mellitus[mh] AND treatment[tiab] AND systematic review[pt] AND 2023:2024[dp]

Clinical trials comparing two drugs

对比两种药物的临床试验

(metformin[nm] OR insulin[nm]) AND diabetes mellitus, type 2[mh] AND randomized controlled trial[pt]
(metformin[nm] OR insulin[nm]) AND diabetes mellitus, type 2[mh] AND randomized controlled trial[pt]

Author-specific research

特定作者2023年癌症相关英文研究

smith ja[au] AND cancer[tiab] AND 2023[dp] AND english[la]

**When to consult search_syntax.md**:
- Need comprehensive list of available field tags
- Require detailed explanation of search operators
- Constructing complex proximity searches
- Understanding automatic term mapping behavior
- Need specific syntax for date ranges, wildcards, or special characters

Grep pattern for field tags: `\[au\]|\[ti\]|\[ab\]|\[mh\]|\[pt\]|\[dp\]`
smith ja[au] AND cancer[tiab] AND 2023[dp] AND english[la]

**何时参考search_syntax.md**：
- 需要完整的字段标签列表
- 需要检索运算符的详细说明
- 构建复杂邻近检索
- 了解自动术语映射机制
- 需要日期范围、通配符或特殊字符的特定语法

字段标签匹配正则：`\[au\]|\[ti\]|\[ab\]|\[mh\]|\[pt\]|\[dp\]`

2. MeSH Terms and Controlled Vocabulary

2. MeSH术语与受控词汇

Use Medical Subject Headings (MeSH) for precise, consistent searching across the biomedical literature.
MeSH Searching:
  • [mh] tag searches MeSH terms with automatic inclusion of narrower terms
  • [majr] tag limits to articles where the topic is the main focus
  • Combine MeSH terms with subheadings for specificity (e.g., diabetes mellitus/therapy[mh])
Common MeSH Subheadings:
  • /diagnosis - Diagnostic methods
  • /drug therapy - Pharmaceutical treatment
  • /epidemiology - Disease patterns and prevalence
  • /etiology - Disease causes
  • /prevention & control - Preventive measures
  • /therapy - Treatment approaches
Example:
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使用医学主题词（MeSH）在生物医学文献中进行精准、一致的检索。
MeSH检索方式：
  • [mh]标签检索MeSH术语，自动包含更窄的下位术语
  • [majr]标签限定检索主题为文献重点的记录
  • 结合MeSH术语与副标题以提高特异性（例如：diabetes mellitus/therapy[mh]）
常用MeSH副标题：
  • /diagnosis - 诊断方法
  • /drug therapy - 药物治疗
  • /epidemiology - 疾病模式与患病率
  • /etiology - 疾病病因
  • /prevention & control - 预防措施
  • /therapy - 治疗方法
示例：
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Diabetes therapy with specific focus

针对2型糖尿病药物治疗及心血管疾病预防的检索

diabetes mellitus, type 2[mh]/drug therapy AND cardiovascular diseases[mh]/prevention & control
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diabetes mellitus, type 2[mh]/drug therapy AND cardiovascular diseases[mh]/prevention & control
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3. Article Type and Publication Filtering

3. 文献类型与发表信息筛选

Filter results by publication type, date, text availability, and other attributes.
Publication Types (use [pt] field tag):
  • Clinical Trial
  • Meta-Analysis
  • Randomized Controlled Trial
  • Review
  • Systematic Review
  • Case Reports
  • Guideline
Date Filtering:
  • Single year: 
    2024[dp]
  • Date range: 
    2020:2024[dp]
  • Specific date: 
    2024/03/15[dp]
Text Availability:
  • Free full text: Add 
    AND free full text[sb]
    to query
  • Has abstract: Add 
    AND hasabstract[text]
    to query
Example:
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根据文献类型、日期、文本可用性等属性筛选结果。
文献类型（使用[pt]字段标签）：
  • Clinical Trial（临床试验）
  • Meta-Analysis（荟萃分析）
  • Randomized Controlled Trial（随机对照试验）
  • Review（综述）
  • Systematic Review（系统综述）
  • Case Reports（病例报告）
  • Guideline（指南）
日期筛选：
  • 单一年份：
    2024[dp]
  • 日期范围：
    2020:2024[dp]
  • 特定日期：
    2024/03/15[dp]
文本可用性：
  • 免费全文：在查询中添加
    AND free full text[sb]
  • 含摘要：在查询中添加
    AND hasabstract[text]
示例：
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Recent free full-text RCTs on hypertension

2023-2024年高血压相关免费全文随机对照试验

hypertension[mh] AND randomized controlled trial[pt] AND 2023:2024[dp] AND free full text[sb]
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hypertension[mh] AND randomized controlled trial[pt] AND 2023:2024[dp] AND free full text[sb]
undefined

4. Programmatic Access via E-utilities API

4. 通过E-utilities API编程访问

Access PubMed data programmatically using the NCBI E-utilities REST API for automation and bulk operations.
Core API Endpoints:
  1. ESearch - Search database and retrieve PMIDs
  2. EFetch - Download full records in various formats
  3. ESummary - Get document summaries
  4. EPost - Upload UIDs for batch processing
  5. ELink - Find related articles and linked data
Basic Workflow:
python
import requests
使用NCBI E-utilities REST API以编程方式访问PubMed数据，实现自动化和批量操作。
核心API端点：
  1. ESearch - 检索数据库并获取PMID
  2. EFetch - 下载多种格式的完整记录
  3. ESummary - 获取文献摘要信息
  4. EPost - 上传UID用于批量处理
  5. ELink - 查找相关文献和关联数据
基础工作流：
python
import requests

Step 1: Search for articles

步骤1：检索文献

base_url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/" search_url = f"{base_url}esearch.fcgi" params = { "db": "pubmed", "term": "diabetes[tiab] AND 2024[dp]", "retmax": 100, "retmode": "json", "api_key": "YOUR_API_KEY" # Optional but recommended } response = requests.get(search_url, params=params) pmids = response.json()["esearchresult"]["idlist"]
base_url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/" search_url = f"{base_url}esearch.fcgi" params = { "db": "pubmed", "term": "diabetes[tiab] AND 2024[dp]", "retmax": 100, "retmode": "json", "api_key": "YOUR_API_KEY" # 可选但推荐使用 } response = requests.get(search_url, params=params) pmids = response.json()["esearchresult"]["idlist"]

Step 2: Fetch article details

步骤2：获取文献详情

fetch_url = f"{base_url}efetch.fcgi" params = { "db": "pubmed", "id": ",".join(pmids), "rettype": "abstract", "retmode": "text", "api_key": "YOUR_API_KEY" } response = requests.get(fetch_url, params=params) abstracts = response.text

**Rate Limits**:
- Without API key: 3 requests/second
- With API key: 10 requests/second
- Always include User-Agent header

**Best Practices**:
- Use history server (usehistory=y) for large result sets
- Implement batch operations via EPost for multiple UIDs
- Cache results locally to minimize redundant calls
- Respect rate limits to avoid service disruption

**When to consult api_reference.md**:
- Need detailed endpoint documentation
- Require parameter specifications for each E-utility
- Constructing batch operations or history server workflows
- Understanding response formats (XML, JSON, text)
- Troubleshooting API errors or rate limit issues

Grep pattern for API endpoints: `esearch|efetch|esummary|epost|elink|einfo`
fetch_url = f"{base_url}efetch.fcgi" params = { "db": "pubmed", "id": ",".join(pmids), "rettype": "abstract", "retmode": "text", "api_key": "YOUR_API_KEY" } response = requests.get(fetch_url, params=params) abstracts = response.text

**速率限制**：
- 无API密钥：3次请求/秒
- 有API密钥：10次请求/秒
- 必须包含User-Agent请求头

**最佳实践**：
- 对大型结果集使用历史服务器（usehistory=y）
- 通过EPost实现多UID的批量操作
- 本地缓存结果以减少重复请求
- 遵守速率限制避免服务中断

**何时参考api_reference.md**：
- 需要详细的端点文档
- 需要每个E-utility的参数说明
- 构建批量操作或历史服务器工作流
- 了解响应格式（XML、JSON、文本）
- 排查API错误或速率限制问题

API端点匹配正则：`esearch|efetch|esummary|epost|elink|einfo`

5. Citation Matching and Article Retrieval

5. 文献匹配与获取

Find articles using partial citation information or specific identifiers.
By Identifier:
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通过部分引用信息或特定标识符查找文献。
通过标识符检索：
undefined

By PMID

通过PMID

12345678[pmid]
12345678[pmid]

By DOI

通过DOI

10.1056/NEJMoa123456[doi]
10.1056/NEJMoa123456[doi]

By PMC ID

通过PMC ID

PMC123456[pmc]

**Citation Matching** (via ECitMatch API):
Use journal name, year, volume, page, and author to find PMIDs:
Format: journal|year|volume|page|author|key| Example: Science|2008|320|5880|1185|key1|

**By Author and Metadata**:
PMC123456[pmc]

**文献匹配**（通过ECitMatch API）：
使用期刊名称、年份、卷、页码和作者信息查找PMID：
格式：journal|year|volume|page|author|key| 示例：Science|2008|320|5880|1185|key1|

**通过作者和元数据检索**：

First author with year and topic

第一作者+年份+主题

smith ja[1au] AND 2023[dp] AND cancer[tiab]
smith ja[1au] AND 2023[dp] AND cancer[tiab]

Journal, volume, and page

期刊+卷+页码

nature[ta] AND 2024[dp] AND 456[vi] AND 123-130[pg]
undefined
nature[ta] AND 2024[dp] AND 456[vi] AND 123-130[pg]
undefined

6. Systematic Literature Reviews

6. 系统文献综述

Conduct comprehensive literature searches for systematic reviews and meta-analyses.
PICO Framework (Population, Intervention, Comparison, Outcome): Structure clinical research questions systematically:
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开展全面的文献检索以支持系统综述和荟萃分析。
PICO框架（人群、干预、对照、结局）： 系统构建临床研究问题：
undefined

Example: Diabetes treatment effectiveness

示例：糖尿病治疗效果

P: diabetes mellitus, type 2[mh]

P: diabetes mellitus, type 2[mh]

I: metformin[nm]

I: metformin[nm]

C: lifestyle modification[tiab]

C: lifestyle modification[tiab]

O: glycemic control[tiab]

O: glycemic control[tiab]

diabetes mellitus, type 2[mh] AND (metformin[nm] OR lifestyle modification[tiab]) AND glycemic control[tiab] AND randomized controlled trial[pt]

**Comprehensive Search Strategy**:
diabetes mellitus, type 2[mh] AND (metformin[nm] OR lifestyle modification[tiab]) AND glycemic control[tiab] AND randomized controlled trial[pt]

**全面检索策略**：

Include multiple synonyms and MeSH terms

包含多个同义词和MeSH术语

(disease name[tiab] OR disease name[mh] OR synonym[tiab]) AND (treatment[tiab] OR therapy[tiab] OR intervention[tiab]) AND (systematic review[pt] OR meta-analysis[pt] OR randomized controlled trial[pt]) AND 2020:2024[dp] AND english[la]

**Search Refinement**:
1. Start broad, review results
2. Add specificity with field tags
3. Apply date and publication type filters
4. Use Advanced Search to view query translation
5. Combine search history for complex queries

**When to consult common_queries.md**:
- Need example queries for specific disease types or research areas
- Require templates for different study designs
- Looking for population-specific query patterns (pediatric, geriatric, etc.)
- Constructing methodology-specific searches
- Need quality filters or best practice patterns

Grep pattern for query examples: `diabetes|cancer|cardiovascular|clinical trial|systematic review`
(disease name[tiab] OR disease name[mh] OR synonym[tiab]) AND (treatment[tiab] OR therapy[tiab] OR intervention[tiab]) AND (systematic review[pt] OR meta-analysis[pt] OR randomized controlled trial[pt]) AND 2020:2024[dp] AND english[la]

**检索优化**：
1. 先进行宽泛检索，查看结果
2. 添加字段标签提高特异性
3. 应用日期和文献类型筛选
4. 使用高级检索查看查询翻译结果
5. 结合检索历史构建复杂查询

**何时参考common_queries.md**：
- 需要特定疾病类型或研究领域的查询示例
- 需要不同研究设计的模板
- 寻找特定人群的查询模式（儿科、老年科等）
- 构建方法学相关检索
- 需要质量筛选或最佳实践模式

查询示例匹配正则：`diabetes|cancer|cardiovascular|clinical trial|systematic review`

7. Search History and Saved Searches

7. 检索历史与保存检索

Use PubMed's search history and My NCBI features for efficient research workflows.
Search History (via Advanced Search):
  • Maintains up to 100 searches
  • Expires after 8 hours of inactivity
  • Combine previous searches using # references
  • Preview result counts before executing
Example:
#1: diabetes mellitus[mh]
#2: cardiovascular diseases[mh]
#3: #1 AND #2 AND risk factors[tiab]
My NCBI Features:
  • Save searches indefinitely
  • Set up email alerts for new matching articles
  • Create collections of saved articles
  • Organize research by project or topic
RSS Feeds: Create RSS feeds for any search to monitor new publications in your area of interest.
使用PubMed的检索历史和My NCBI功能优化研究工作流。
检索历史（通过高级检索）：
  • 最多保存100次检索
  • 8小时无操作后过期
  • 使用#引用结合之前的检索
  • 执行前预览结果数量
示例：
#1: diabetes mellitus[mh]
#2: cardiovascular diseases[mh]
#3: #1 AND #2 AND risk factors[tiab]
My NCBI功能：
  • 永久保存检索
  • 设置邮件提醒获取新匹配文献
  • 创建已保存文献的集合
  • 按项目或主题组织研究内容
RSS订阅： 为任意检索创建RSS订阅，持续关注研究领域的新发表文献。

8. Related Articles and Citation Discovery

8. 相关文献与引文发现

Find related research and explore citation networks.
Similar Articles Feature: Every PubMed article includes pre-calculated related articles based on:
  • Title and abstract similarity
  • MeSH term overlap
  • Weighted algorithmic matching
ELink for Related Data:
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查找相关研究并探索引文网络。
相似文献功能： 每篇PubMed文献都包含预计算的相关文献，基于：
  • 标题和摘要相似度
  • MeSH术语重叠
  • 加权算法匹配
通过ELink获取相关数据：
undefined

Find related articles programmatically

以编程方式查找相关文献

elink.fcgi?dbfrom=pubmed&db=pubmed&id=PMID&cmd=neighbor

**Citation Links**:
- LinkOut to full text from publishers
- Links to PubMed Central free articles
- Connections to related NCBI databases (GenBank, ClinicalTrials.gov, etc.)
elink.fcgi?dbfrom=pubmed&db=pubmed&id=PMID&cmd=neighbor

**引文链接**：
- 链接到出版商提供的全文
- 链接到PubMed Central免费文献
- 关联到NCBI相关数据库（GenBank、ClinicalTrials.gov等）

9. Export and Citation Management

9. 导出与文献管理

Export search results in various formats for citation management and further analysis.
Export Formats:
  • .nbib files for reference managers (Zotero, Mendeley, EndNote)
  • AMA, MLA, APA, NLM citation styles
  • CSV for data analysis
  • XML for programmatic processing
Clipboard and Collections:
  • Clipboard: Temporary storage for up to 500 items (8-hour expiration)
  • Collections: Permanent storage via My NCBI account
Batch Export via API:
python
undefined
以多种格式导出检索结果，用于文献管理和进一步分析。
导出格式：
  • .nbib文件（兼容Zotero、Mendeley、EndNote等参考文献管理工具）
  • AMA、MLA、APA、NLM等引文格式
  • CSV格式（用于数据分析）
  • XML格式（用于编程处理）
剪贴板与集合：
  • 剪贴板：临时存储最多500条记录（8小时后过期）
  • 集合：通过My NCBI账户永久存储
通过API批量导出：
python
undefined

Export citations in MEDLINE format

以MEDLINE格式导出引文

efetch.fcgi?db=pubmed&id=PMID1,PMID2&rettype=medline&retmode=text
undefined
efetch.fcgi?db=pubmed&id=PMID1,PMID2&rettype=medline&retmode=text
undefined

Working with Reference Files

参考文献文件使用

This skill includes three comprehensive reference files in the 
references/
directory:
本技能在
references/
目录下包含三份完整的参考文件：

references/api_reference.md

references/api_reference.md

Complete E-utilities API documentation including all nine endpoints, parameters, response formats, and best practices. Consult when:
  • Implementing programmatic PubMed access
  • Constructing API requests
  • Understanding rate limits and authentication
  • Working with large datasets via history server
  • Troubleshooting API errors
完整的E-utilities API文档，涵盖所有9个端点、参数、响应格式和最佳实践。在以下场景参考：
  • 实现PubMed编程访问
  • 构建API请求
  • 了解速率限制和认证机制
  • 通过历史服务器处理大型数据集
  • 排查API错误

references/search_syntax.md

references/search_syntax.md

Detailed guide to PubMed search syntax including field tags, Boolean operators, wildcards, and special characters. Consult when:
  • Constructing complex search queries
  • Understanding automatic term mapping
  • Using advanced search features (proximity, wildcards)
  • Applying filters and limits
  • Troubleshooting unexpected search results
PubMed检索语法详细指南，包含字段标签、布尔运算符、通配符和特殊字符。在以下场景参考：
  • 构建复杂检索查询
  • 了解自动术语映射
  • 使用高级检索功能（邻近检索、通配符）
  • 应用筛选和限制条件
  • 排查意外检索结果

references/common_queries.md

references/common_queries.md

Extensive collection of example queries for various research scenarios, disease types, and methodologies. Consult when:
  • Starting a new literature search
  • Need templates for specific research areas
  • Looking for best practice query patterns
  • Conducting systematic reviews
  • Searching for specific study designs or populations
Reference Loading Strategy: Load reference files into context as needed based on the specific task. For brief queries or basic searches, the information in this SKILL.md may be sufficient. For complex operations, consult the appropriate reference file.
涵盖各种研究场景、疾病类型和方法学的大量查询示例。在以下场景参考：
  • 启动新的文献检索
  • 需要特定研究领域的模板
  • 寻找最佳实践查询模式
  • 开展系统综述
  • 检索特定研究设计或人群的文献
参考文件加载策略： 根据具体任务按需加载参考文件。对于简单查询或基础检索，本SKILL.md中的信息已足够。对于复杂操作，参考对应的参考文件。

Common Workflows

常见工作流

Workflow 1: Basic Literature Search

工作流1：基础文献检索

  1. Identify key concepts and synonyms
  2. Construct query with Boolean operators and field tags
  3. Review initial results and refine query
  4. Apply filters (date, article type, language)
  5. Export results for analysis
  1. 确定核心概念和同义词
  2. 使用布尔运算符和字段标签构建查询
  3. 查看初始结果并优化查询
  4. 应用筛选条件（日期、文献类型、语言）
  5. 导出结果用于分析

Workflow 2: Systematic Review Search

工作流2：系统综述检索

  1. Define research question using PICO framework
  2. Identify all relevant MeSH terms and synonyms
  3. Construct comprehensive search strategy
  4. Search multiple databases (include PubMed)
  5. Document search strategy and date
  6. Export results for screening and review
  1. 使用PICO框架定义研究问题
  2. 确定所有相关MeSH术语和同义词
  3. 构建全面检索策略
  4. 检索多个数据库（包括PubMed）
  5. 记录检索策略和日期
  6. 导出结果用于筛选和综述

Workflow 3: Programmatic Data Extraction

工作流3：编程式数据提取

  1. Design search query and test in web interface
  2. Implement search using ESearch API
  3. Use history server for large result sets
  4. Retrieve detailed records with EFetch
  5. Parse XML/JSON responses
  6. Store data locally with caching
  7. Implement rate limiting and error handling
  1. 设计检索查询并在网页界面测试
  2. 使用ESearch API实现检索
  3. 对大型结果集使用历史服务器
  4. 使用EFetch获取详细记录
  5. 解析XML/JSON响应
  6. 本地存储数据并缓存
  7. 实现速率限制和错误处理

Workflow 4: Citation Discovery

工作流4：引文发现

  1. Start with known relevant article
  2. Use Similar Articles to find related work
  3. Check citing articles (when available)
  4. Explore MeSH terms from relevant articles
  5. Construct new searches based on discoveries
  6. Use ELink to find related database entries
  1. 从已知相关文献开始
  2. 使用相似文献功能查找相关研究
  3. 查看引用该文献的其他文献（若可用）
  4. 探索相关文献的MeSH术语
  5. 根据发现构建新检索
  6. 使用ELink查找相关数据库条目

Workflow 5: Ongoing Literature Monitoring

工作流5：持续文献监测

  1. Construct comprehensive search query
  2. Test and refine query for precision
  3. Save search to My NCBI account
  4. Set up email alerts for new matches
  5. Create RSS feed for feed reader monitoring
  6. Review new articles regularly
  1. 构建全面检索查询
  2. 测试并优化查询以提高精准度
  3. 将检索保存到My NCBI账户
  4. 设置邮件提醒获取新匹配文献
  5. 创建RSS订阅用于阅读器监测
  6. 定期查看新文献

Tips and Best Practices

技巧与最佳实践

Search Strategy

检索策略

  • Start broad, then narrow with field tags and filters
  • Include synonyms and MeSH terms for comprehensive coverage
  • Use quotation marks for exact phrases
  • Check Search Details in Advanced Search to verify query translation
  • Combine multiple searches using search history
  • 先宽泛检索，再通过字段标签和筛选条件缩小范围
  • 包含同义词和MeSH术语以确保全面覆盖
  • 使用双引号进行精确短语匹配
  • 在高级检索中查看检索详情以验证查询翻译
  • 使用检索历史组合多个检索

API Usage

API使用

  • Obtain API key for higher rate limits (10 req/sec vs 3 req/sec)
  • Use history server for result sets > 500 articles
  • Implement exponential backoff for rate limit handling
  • Cache results locally to minimize redundant requests
  • Always include descriptive User-Agent header
  • 获取API密钥以获得更高速率限制（10次请求/秒 vs 3次请求/秒）
  • 对超过500篇文献的结果集使用历史服务器
  • 实现指数退避机制处理速率限制
  • 本地缓存结果以减少重复请求
  • 始终包含描述性的User-Agent请求头

Quality Filtering

质量筛选

  • Prefer systematic reviews and meta-analyses for synthesized evidence
  • Use publication type filters to find specific study designs
  • Filter by date for most recent research
  • Apply language filters as appropriate
  • Use free full text filter for immediate access
  • 优先选择系统综述和荟萃分析以获取综合证据
  • 使用文献类型筛选查找特定研究设计
  • 根据日期筛选获取最新研究
  • 按需应用语言筛选
  • 使用免费全文筛选立即获取可访问文献

Citation Management

文献管理

  • Export early and often to avoid losing search results
  • Use .nbib format for compatibility with most reference managers
  • Create My NCBI account for permanent collections
  • Document search strategies for reproducibility
  • Use Collections to organize research by project
  • 尽早且频繁导出结果避免丢失
  • 使用.nbib格式以兼容大多数参考文献管理工具
  • 创建My NCBI账户以永久保存集合
  • 记录检索策略以保证可重复性
  • 使用集合按项目组织研究内容

Limitations and Considerations

局限性与注意事项

Database Coverage

数据库覆盖范围

  • Primarily biomedical and life sciences literature
  • Pre-1975 articles often lack abstracts
  • Full author names available from 2002 forward
  • Non-English abstracts available but may default to English display
  • 主要涵盖生物医学和生命科学文献
  • 1975年之前的文献通常无摘要
  • 2002年之后的文献包含完整作者姓名
  • 非英文摘要可用，但默认显示英文版本

Search Limitations

检索局限性

  • Display limited to 10,000 results maximum
  • Search history expires after 8 hours of inactivity
  • Clipboard holds max 500 items with 8-hour expiration
  • Automatic term mapping may produce unexpected results
  • 最多显示10,000条结果
  • 检索历史在8小时无操作后过期
  • 剪贴板最多存储500条记录，8小时后过期
  • 自动术语映射可能产生意外结果

API Considerations

API注意事项

  • Rate limits apply (3-10 requests/second)
  • Large queries may time out (use history server)
  • XML parsing required for detailed data extraction
  • API key recommended for production use
  • 存在速率限制（3-10次请求/秒）
  • 大型查询可能超时（使用历史服务器解决）
  • 详细数据提取需要解析XML
  • 生产环境建议使用API密钥

Access Limitations

访问局限性

  • PubMed provides citations and abstracts (not always full text)
  • Full text access depends on publisher, institutional access, or open access status
  • LinkOut availability varies by journal and institution
  • Some content requires subscription or payment
  • PubMed提供引文和摘要（不总是提供全文）
  • 全文访问取决于出版商、机构权限或开放获取状态
  • LinkOut可用性因期刊和机构而异
  • 部分内容需要订阅或付费

Support Resources

支持资源