Article(id=1322165016271368565, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322164984558243894, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.01.014, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1750262400000, receivedDateStr=2025-06-19, revisedDate=1764691200000, revisedDateStr=2025-12-03, acceptedDate=null, acceptedDateStr=null, onlineDate=1793158304516, onlineDateStr=2026-10-28, pubDate=1767974400000, pubDateStr=2026-01-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793158304516, onlineIssueDateStr=2026-10-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793158304516, creator=13701087609, updateTime=1793158304516, updator=13701087609, issue=Issue{id=1322164984558243894, tenantId=1146029695717560320, journalId=1220038315760373764, year='2026', volume='40', issue='1', pageStart='1', pageEnd='176', issueExtLink='null', onlineDate='null', pubDate='1767974400000', pubDateStr='2026-01-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793158296955, creator='13701087609', updateTime=1793158296955, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=102, endPage=107, ext={EN=ArticleExt(id=1322165016611107190, articleId=1322165016271368565, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Ethical risks and countermeasures of applying large language models in nursing research, columnId=1220689487877821128, journalTitle=Chinese Nursing Research, columnName=Interdisciplinary Nursing Study, runingTitle=null, highlight=null, articleAbstract=

This paper systematically outlined the current application status of large language models in nursing academic research,analyzed the potential ethical risks they may pose to the research process,and identified influencing factors of these risks from technical,institutional,and user perspectives.Ethical countermeasures are proposed from four angles:technology developers,users,managers,and society,to promote the standardized application of large language models in nursing research.

, authors=Kongjun JIN1, Yiqin SUN1, Shuiqin XU1, Ci LI2, authorsList=Kongjun JIN, Yiqin SUN, Shuiqin XU, Ci LI, authorCompany=null, correspAuthors=Kongjun JIN, authorNote=null, correspAuthorsNote=
金孔军,助教,硕士研究生,E-mail:
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系统概述大语言模型在护理学术科研中的应用现状,分析其可能会对科研过程带来的伦理风险,从技术、制度和使用者3个层面列举伦理风险出现的影响因素,并从技术研发者角度、使用者角度、管理者角度、社会角度4个层面提出伦理应对策略,从而促进大语言模型在护理科研中规范化应用。

, authors=金孔军1, 孙一勤1, 徐水琴1, 李辞2, authorsList=金孔军, 孙一勤, 徐水琴, 李辞, authorCompany=null, correspAuthors=金孔军, authorNote=null, correspAuthorsNote=
JIN Kongjun, E-mail:
金孔军,助教,硕士研究生,E-mail:
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护理科研中应用大语言模型的伦理风险与应对策略
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金孔军 1 , 孙一勤 1 , 徐水琴 1 , 李辞 2
护理研究 | 交叉护理学研究 2026,40(1): 102-107
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护理研究 |交叉护理学研究 2026 , 40 (1) : 102 -107
护理科研中应用大语言模型的伦理风险与应对策略
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金孔军1 , 孙一勤1, 徐水琴1, 李辞2
作者信息
  • 1绍兴文理学院医学院,浙江 312000
  • 2首都医科大学护理学院
通讯作者:
JIN Kongjun, E-mail:
金孔军,助教,硕士研究生,E-mail:
Ethical risks and countermeasures of applying large language models in nursing research
Kongjun JIN1 , Yiqin SUN1, Shuiqin XU1, Ci LI2
Affiliations
  • 1School of Medicine, Shaoxing University, Zhejiang 312000 China
  • 2School of Nursing, Capital Medical University
  • Corresponding Author:
    金孔军,助教,硕士研究生,E-mail:
出版时间: 2026-01-10 doi: 10.12102/j.issn.1009-6493.2026.01.014
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系统概述大语言模型在护理学术科研中的应用现状,分析其可能会对科研过程带来的伦理风险,从技术、制度和使用者3个层面列举伦理风险出现的影响因素,并从技术研发者角度、使用者角度、管理者角度、社会角度4个层面提出伦理应对策略,从而促进大语言模型在护理科研中规范化应用。

大语言模型  /  护理  /  伦理  /  护理科研  /  应对策略

This paper systematically outlined the current application status of large language models in nursing academic research,analyzed the potential ethical risks they may pose to the research process,and identified influencing factors of these risks from technical,institutional,and user perspectives.Ethical countermeasures are proposed from four angles:technology developers,users,managers,and society,to promote the standardized application of large language models in nursing research.

large language models  /  nursing  /  ethics  /  nursing research  /  countermeasures
金孔军, 孙一勤, 徐水琴, 李辞. 护理科研中应用大语言模型的伦理风险与应对策略. 护理研究, 2026 , 40 (1) : 102 -107 . DOI: 10.12102/j.issn.1009-6493.2026.01.014
Kongjun JIN, Yiqin SUN, Shuiqin XU, Ci LI. Ethical risks and countermeasures of applying large language models in nursing research[J]. Chinese Nursing Research, 2026 , 40 (1) : 102 -107 . DOI: 10.12102/j.issn.1009-6493.2026.01.014
随着科学技术不断进步,护理学科发展也在不断向纵深化、精细化发展。《全国护理事业发展规划(2021—2025年)》[1]提出,应充分、高效利用云计算、人工智能等现代技术手段为病人提供便捷、有效的现代护理服务。大语言模型(large language models,LLMs)是通过深度学习训练而形成的语言模型,是近年来出现的现象级人工智能产品。其基本原理是利用生成式人工智能(AI-generated content,AIGC)模拟人类创造性思维展现出强大的类人能力,用户可以通过输入指令,大语言模型输出新的文本、音频、代码等内容[2],具有数据巨量化、内容创造力、认知交互力等特征。在护理场景中大语言模型应用主要包括护理教育(个性化教学、模拟病例)、临床实践(诊断辅助、生成护理计划)、科研支持(数据挖掘、论文撰写)等。在护理科研数据提取中,大语言模型可以自动化处理海量文本数据(如电子病历分析、文献综述生成),显著节省人力成本,提升研究效率。在健康知识普及中,大语言模型可以突破语言壁垒,促进病人健康知识获取与方便医患沟通。在护理研究赋能中,大语言模型可以加速科研流程(如研究方案设计、统计分析),推动资源优化配置。在当前护理实践中,大语言模型应用中47%聚焦教育领域(如模拟教学、考核评估),40%应用于临床实践,科研应用占10%[3],存在护理科研应用不充分的问题。在研究工具层面,研究揭示当前93%文献大语言模型使用以ChatGPT为主,而其他模型科研应用探索不足[3]。在技术缺陷层面,大语言模型存在生成错误信息或强化数据偏见的问题,威胁科研结论可靠性程度[4]。在领域适配性层面,当前大语言模型护理特异性知识库薄弱,对专业术语和临床逻辑理解不足[3,5],可能导致结果输出专业性较差,且部分模型研究缺少护士参与开发或验证,脱离护理实际需求[6]。在当前护理实践中,大语言模型正在重构着知识生产模式,逐步将传统的人机用具关系转化为协同创作关系,但其在护理科研中的快速普及与多样式应用同样引发伦理思考。现通过总结大语言模型在护理科研中的应用现状,分析其可能带来的伦理风险,为护理学术规范应用提供参考。
在文献快速检索和筛选方面,大语言模型可自动化处理文献筛选,通过分析标题和摘要快速识别符合纳入标准的研究,简化复杂或专业性强的摘要文本,降低语言难度水平,帮助外语能力薄弱的学生或护理工作者更容易理解学科内容,增强护理文献阅读体验,扩宽知识传递渠道[7]。该功能能提高科研选题效率,帮助研究人员聚焦核心主题[8]。在优化文献检索策略方面,大语言模型能协助构建文献检索的逻辑搜索字符串,生成标准化检索式,提升检索效率[9]。但当前大语言模型对文献上下文理解不足,需要人工复核、优化检索式以提升检索式灵敏度和特异度。
大语言模型可根据研究者指令自动化生成系统综述中的文献筛选、数据提取和内容生成环节。在文献搭建方面,大语言模型可以通过生成循证PICOS[P(人群)、I(干预)、C(对照)、O(结局)、S(研究类型)]摘要来辅助人工筛选标题和摘要,显著减少人工筛查时间[10]。研究表明,采用大语言模型半自动化(Large Language Models-Half Automation,LLM-HA)和全自动化(Large Language Models-Full Automation,LLM-FA)方法可快速生成文献初稿,如ChatGPT4.0能根据用户指令自动提取数据并生成综述草稿[11],但仍需要人工进一步审核调整修改框架、丰富内容、调整结构及真实性确认。在文献质量评价方面,大语言模型可辅助完成系统综述中的质量评估任务。例如,ChatGPT4.0在当前循证科研的非随机干预研究偏倚风险评估(ROBINS-I工具)中展现出与人类评审员的高度一致性,为循证实践提供标准化分析框架[12]。但当前部分大语言模型缺乏标准化评估框架,会导致模型输出结果出现错误。
大语言模型发展成为打破学科壁垒的有效工具,其能够实现不同知识体系的重构,帮助研究者完成跨学科的知识融合,为护理学术研究带来了新技术及新研究范式。护理研究常涉及医学、心理学、社会学等多学科知识,大模型可提取不同领域文献中的关联信息。在不同学科文献合成和知识挖掘层面,大语言模型被用于自动处理生物医学文献和临床数据,整合多模态信息(如文本、图像和视频),从而描绘复杂的关系。例如,大语言模型能合成生物医学文献,揭示跨学科关联(如心理学与生理学的相互作用)[13]。在学科知识整合层面,大语言模型与医学知识图谱结合,可以增强护理知识和灵感启发。例如在生物医学任务中整合大语言模型与知识图谱,能够更准确地建模疾病机制或治疗方案,为护理的跨学科研究提供新途径[14]。然而,整合需要满足数据源一致和数据标准化需求,需要强化大模型技术优化。
在文章语言优化与编辑方面,大语言模型可以帮助研究者提升学术表达的准确性与流畅性。例如,Marchandot等[15]提出对于目前在编撰的文献,利用ChatGPT能够帮助语法纠正、疏通条理及提出修改建议等。为医学稿件提供语言润色服务,增强术语一致性与逻辑连贯性,辅助非英语母语研究者提高英语学术写作能力[16]。在术语标准化方面,大语言模型可以通过识别医学文本中的语义变体(如同义词、缩写),在标准化电子健康记录等非结构化文本中,大语言模型可以理解上下文使其变为统一文字,保持数据可靠性[17]。在文稿风格上,大语言模型能统一写作风格和术语使用,在多人合作的撰稿文章中,确保稿件整体协调性[18]。在排版与格式规范化方面,大语言模型可依据期刊要求自动生成符合规范的标题、摘要、参考文献格式。但当前各期刊对于大语言模型的使用有不同要求,因为论文在润色过程中可能会出现增加幻觉内容等问题,需要研究者严格内容把控。
在护理科研设计中,刘宝存等[19]提出可以使用ChatGPT辅助完善科研设计,完成框架搭建或完成漏洞评估等。在研究思维启发方面,对于研究疑问,ChatGPT可以根据指令自动生成500词左右的答案[20],并对数据进行分析及可视化处理,有助于研究者理清研究思路,启发研究灵感。在研究设计中,大语言模型可以用于生成研究报告草案和模拟训练,从而充当护理科研设计的辅助工具。并且,大语言模型可被整合用于评估框架开发,以验证模型在临床研究中的适用性,确保研究方法的稳健性[21]。研究方案生成与优化层面,大语言模型能够快速生成临床研究方案(如试验协议、知情同意书等)的初稿,提高文档撰写效率。通过检索增强生成等技术,模型可基于特定疾病和试验阶段生成内容,并接受人工评估与优化迭代[22]。例如,在临床试验协议设计中,大语言模型可辅助生成研究目标、纳入和排除标准等核心内容[23],但对于大语言模型的输出结果,需结合专家审核以确保科学性和合规性。
在研究数据处理方面,大语言模型可以自动提取和处理研究数据,从已发表文献中提取数据(如关键词、结果和统计),支持大型数据集的处理和分析,减轻人工负担,有助于加速数据收集,能高效处理研究论文[24]。在研究文本处理方面,大语言模型在护理定性研究方法中用于数据编码、分析和整合,帮助处理和分析访谈或观察数据,实现混合方法研究的三角化实证,以增强研究的可靠性和深度[25]。实证研究显示,两个大语言模型同时分析半结构化访谈数据时,提取的主题高度一致[26]。因此,其能够减少定性研究中人工分析的时间和资源消耗,并且可以促进护理学术研究的方法论创新[27]。大语言模型在研究数据可视化与结果解读层面,其可将统计结果(如干预组与对照组的疗效差异)转化为自然语言描述,并生成图表说明,降低非统计专业研究者的结果解读门槛,但存在数据的真实性挑战。
护理科研与学术数据通常包含敏感的病人健康信息,其数据安全性和保密性尤为重要。例如颜见智等[28]提出在科研数据中,如发生敏感信息的泄漏和滥用,可能会引发医疗机构病人信任度降低、激化医疗矛盾等问题。并且大语言模型可能对研究数据的合法使用形成挑战。例如大语言模型在深度的学习和训练中,可能持续挖掘人类的偏向隐私信息(如脸部识别、指纹、账号等),以形成更加智慧的模型[29]。并且在长期的训练和对话中,可能存在种族、语言、地域等偏见,例如Mann[30]提出大语言模型会因为地域限制、技术限制等原因输出有偏差的结果。因此,在使用大语言模型处理数据时,需要确保数据保护和保密,如涉及机密与隐私性的实验数据、研究设计等,需要预防数据的后台收集和传播。
当前使用大语言模型辅助科研实践愈发普遍[8]。使用大语言模型辅助生成论文内容时,研究贡献与论文作者身份确认会存在冲突。主体主义赋权论认为通过算法、规则、模板和模型训练的生成物不能体现作者的个性思想[31],不能进行独创署名。例如Nature、Science、Elsevier及出版商也强调大语言模型不能作为作者进行署名,如果使用则需要在文中方法部分或致谢中进行说明[32-34]。
大语言模型是创新研究的辅助制造工具,其生成的内容如果构成研究的核心创新点,创新的知识产权归属权就会陷入争议。例如秦艳华等[35]指出大语言模型在论文应用中存在隐形过滤、版权不清、数据窃取等问题,出版业需高度警惕和重视。在持续研究过程中,一旦大语言模型生成的文本被用作研究成果的一部分,后期判断这些文本权益判定将变得更加困难。
大语言模型的数据来源、质量和真实性对输出结果有重要影响。当前大语言模型生成信息呈现“拼接式”特点,在利用大语言模型辅助制定研究方案时,模型产生的答案在抓取期刊等主流信息的同时也会剪取网页问答等低质量答案,信息质量参差不齐,影响研究者的综合参考。例如Bender等[36]指出大语言模型面临着信息评估不全面、结果难以复现等问题,其过程不透明导致难以实现结果的逆推。幻觉是目前大语言模型存在的重要挑战,即在护患进行人机沟通时,大语言模型学习并模仿设定的写作风格会给出看似严谨、科学,但实际是虚假的、拼接的错误答案[37],这给科研过程和结果的真实性带来严重挑战。
大语言模型的AIGC技术可以根据指令持续生成新式文本内容,例如ChatGPT可以生成格式完整、逻辑通畅的研究论文[38],特别是当前大语言模型类别众多,持续生成的新文本均未被系统检测记录。有研究显示,ChatGPT生成的摘要可以通过原创性检测,这会给同行评审带来误判[39],因此对于学术评价带来错误导向。在学生层面,学生还可能存在过度依赖使用大语言模型工具,从而失去自身的评判思维和创造能力,最终导致学生责任感缺失[20]。在教师使用层面,大语言模型生成的研究案例可能存在信息错拼、价值取向错误等问题,例如研究指出,大语言模型无法保证完全客观和中立,输出文本可能会延续与种族、性别、语言和文化相关的偏见[40]。
大语言模型的生成机制依赖于大规模训练数据,但模型可能产生不准确或虚构内容,误导科研结论。在护理科研中,体现在模型输出缺乏临床或伦理验证,例如在生成研究报告或病人健康建议时传播错误信息。这种幻觉源于模型的“涌现能力”,即基于训练数据自主生成内容时脱离真实性检查[41]。同时,大语言模型的算法偏好存在强化社会偏见问题,如种族或性别偏见的训练数据导致输出内容不公正。例如,商业大语言模型在多场景测试中会传播有害的种族医学内容(例如基于种族而非临床证据分配资源),这在护理科研中可能影响决策的公正性[42]。此外,在技术维度下,大语言模型在护理科研中缺乏标准化评估框架。研究指出,目前常见评估方法为人类专家评审(占26.7%),系统性评价工具缺失导致模型局限未被充分识别[6],且偏见测试或安全验证未被纳入研究流程,会加剧大语言模型输出错误累积[43]。
制度维度涉及政策、标准和指导方针的缺位,其核心在于规范不成熟、伦理框架缺乏和治理规范碎片化。1)在伦理监管指南层面:护理科研领域尚未检索到针对大语言模型的专用伦理框架,导致滥用和误用频发。研究指出,现有制度未提供“有意义的标准”以指导安全使用[5],例如在数据收集或模型部署中缺乏透明原则。此外,36.6%的护理研究完全忽略伦理讨论(如未说明数据使用权限),反映出监管强制力不足[5]。这使得科研实践中可能滋生剽窃或不公正决策,例如模型未经标注生成文本,违反学术诚信[3]。2)在评估标准与基础设施层面,护理科研中的大语言模型应用亟须系统评估,但当前制度层面仍存在缺口。例如有文献指出,当前模型性能测试依赖人工评估,而非标准化指标,这导致模型存在“缺乏系统性评价”局限而未被纳入规范[3]。此外,治理机制缺失(如数据共享协议缺位)还引发责任模糊,例如在电子记录整合中,“缺乏治理和AI责任机制”可能存在责任过失问题[43]。研究强调,当前对于大语言模型学术监管存在“生态系统差异”,例如学术期刊的政策不一致,部分允许大语言模型辅助写作,而部分禁止的情况[5]。
当前使用者维度聚焦使用人员的数字素养技能、使用意识和态度责任。在模型训练护理人员参与方面,研究统计了2010年1月—2021年3月发表的7 610篇人工智能文章,33.3%的论文未纳入护士参与[6],其导致的结果是研究决策可能忽略护理实践视角(如临床伦理复杂性未被反映),这导致大语言模型输出结果脱离护理现实[6]。在伦理意识与技能层面,使用者缺乏AI素养导致存在滥用风险。例如,护理人员教育未整合“AI伦理培训”,研究人员可能过度依赖模型生成结果(如使用Chat-GPT编写报告而未核查和披露),造成信息剽窃或偏见传播[3],难以及时识别模型输出的潜在风险。在使用者责任层面,研究者将大语言模型用于道德决策却缺乏伦理评估。例如,大语言模型被用为分配资源“伦理顾问”时,其存在集体利益偏向而出现系统效率优先而非个体保护情况[44],这可能导致最终输出的结果存在伦理冲突的情况。
大语言模型的文本或数据稳定输出是模型的应用核心,研发公司可以通过制定数据质量标准和使用规范,在模型开发阶段需预置伦理机制,确保大语言模型的数据准确性、完整性和可靠性。例如,加强数据加密与匿名化处理,确保所有病人信息在传输和存储过程中都经过严格加密,并采用匿名化技术处理个人身份信息,减少隐私泄露的风险。在模型结果输出层面,采用多元化训练数据集,通过增加数据集的多样性和代表性,提高算法透明度和可解释性,减少模型中的偏见和歧视。在模型中嵌入偏见监测系统,建立持续审计机制检测健康公平性问题,确保服务的公平性。此外,需要定期更新反偏误数据集,矫正训练数据的历史偏见。对于在实践层面可能出现的大语言模型伦理问题,研究机构可以适当建立反馈机制,及时进行自主报告和风险披露,从而实现实践困境或道德争议的有效规避,维护护理实践的规范性与完整性。
随着大语言模型在各使用层面的普及,需要为护理研究人员、教学管理者和院校学生提供相关培训和教育,确保他们了解和遵守大语言模型使用的隐私保护、公正性、透明度、决策责任等伦理原则内容,理解大语言模型技术原理与局限认知(如理解“幻觉”产生机制)。培养使用者伦理冲突识别技能,训练辨别模型输出中的价值观矛盾,能够在使用初期就知晓应用伦理困境,增强使用者的道德心和责任心。例如通过情景模拟与案例研究、技能培训等方法,持续适应和熟悉大语言模型,培养对事实真伪的判断能力。在人机协作操作规范层面,需要明确界定大语言模型与护理实践者的人机协作边界与角色分工,强调机器无法完全取代人类在情感交流方面的作用。可以通过制定标准操作流程,明确何时采纳与拒绝模型建议。建立反思实践机制,记录LLM使用中的伦理困境并开展案例讨论,促进健康的人机协作关系。
技术的发展始终伴随着监管方式的进步。对于不断出现新的大语言模型的挑战,需定期评估和监管使用规范,确保学术实践始终符合当前的行业标准和法律要求。在文本写作方面,陈潇婷[45]提出可以构建人工智能生成内容(AIGC)驱动的检测技术体系,以准确识别内容间的匹配程度,自动过滤或处理侵权的文本,实现对文本的快速扫描和判断。此外,张强等[37]指出,可以采用机器学习及深度学习算法对ChatGPT生成与学者撰写的摘要文本进行分类检测,判别算法生成的论文部分。颜见智等[28]提出当前的伦理审查机构也需要将大语言模型的伦理和技术进行把控,对于数据处理、储存、偏见等问题进行链式监管,确保模型合法性、合规性、可信度。管理者也可以对大语言模型进行应用风险分级管理,防止自动化偏误扩散,对模型输出添加醒目标识(如“此建议需临床复核”),避免使用人员过度依赖。此外,也可以建立多学科治理委员会,由护理专家、伦理学家、法律人士及技术人员共同制定应用规范,确保大语言模型应用的各维度需求平衡。
随着大语言模型在健康领域的应用增多,在病人健康层面可能引发法律风险逐渐增加,例如带来生成信息不实、违法和不良信息的风险。有研究指出,我国应进一步明确人工智能生成内容致害的侵权责任规则,完善对人工智能生成内容进行标识的要求,区分场景规定不同的标识要求,增加服务使用者和内容传播者的标识义务[46]。当前我国生成式人工智能著作权侵权案件还未有专门的法律进行监管,当前主要由《中华人民共和国著作权法》及《中华人民共和国民法典》进行争议判断。随着版权检测技术与法律法规不匹配问题的日渐凸显,研究者创新积极性与版权冲突矛盾将逐渐加剧,需要有专门法律对大语言模型争议与矛盾进行监管与判定。在政府方面可以在立法与执法层面完善版权侵权的检测和处理的法律框架,确保评判机制与方法的公平公正,在技术使用与创新发展之间找到平衡点[45]。
随着人工智能新技术的不断进步、应用层面不断扩展和使用程度深入,大语言模型在护理实践中的作用将愈发凸显。当前,研究者应清醒地认识到其中蕴含的伦理风险及可能出现的新风险,从技术研发者、使用者、监管者、社会法律等层面采取积极有效的措施加以应对。同时,技术发展是不断演绎更新的过程,将在实践应用中不断产生新的伦理风险,护理工作者需要持续关注并保障病人权益。

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doi: 10.12102/j.issn.1009-6493.2026.01.014
  • 接收时间:2025-06-19
  • 首发时间:2026-10-28
  • 出版时间:2026-01-10
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  • 收稿日期:2025-06-19
  • 修回日期:2025-12-03
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    1绍兴文理学院医学院,浙江 312000
    2首都医科大学护理学院

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JIN Kongjun, E-mail:
金孔军,助教,硕士研究生,E-mail:
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2种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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