「clinical」の検索結果

中医诊疗 — 中医临床医师的认知操作系统 (经典理论 阴阳五行脏腑气血经络六经卫气营血三焦 + 四诊八纲 + 治法 + 方剂学 经方时方 + 中药学 四气五味归经炮制 + 针灸学 经络穴位手法灸法 + 推拿按摩 + 中医各科 内外妇儿骨伤皮肤眼耳鼻喉肛肠老年急症 + 中西医结合 + 循证中医 + NATCM 监管 + 中医诊所备案 + 国家级名老中医 学术继承人 — 不含 道家修炼 / 风水算命 / 中医养生科普 / 中医美容 / 民族医学藏蒙维傣) (Traditional Chinese Medicine (TCM) clinical practice — the cognitive operating system of practicing TCM physicians covering (a) 经典理论 (阴阳/五行/脏腑/气血津液/经络/六淫/七情/六经/卫气营血/三焦) classical theory, (b) 四诊 (望闻问切) clinical observation/inquiry/pulse + 八纲 (阴阳/表里/寒热/虚实) syndrome differentiation, (c) 治法 (汗吐下和温清消补 + 扶正祛邪) therapeutic principles, (d) 方剂学 (经方 时方 验方) formula science, (e) 中药学 (四气五味/归经/七情/炮制/配伍禁忌) materia medica, (f) 针灸学 (经络穴位/手法/灸法/电针) acupuncture+moxibustion, (g) 推拿按摩 tuina massage, (h) 中医各科 (内/外/妇/儿/骨伤/皮肤/眼/耳鼻喉/肛肠/老年/急症) clinical specialties, (i) 中西医结合 integrative medicine, (j) 循证中医 + 临床流行病学 evidence-based TCM + 现代研究, (k) 中医诊所备案 / 中医师资格 / NATCM 监管 / 药典 / GMP regulatory framework, (l) 国家级名老中医 学术继承人 项目 transmission program; NOT 道家修炼 / 风水 / 算命八字 (那是 玄学不是 中医), NOT 中医养生科普 (是 衍生 不是 临床主体), NOT 中医美容 (是 商业化分支), NOT 民族医学 (藏蒙维傣 是 平行体系 不在本 skill 主线 — 仅作 边界标注).) Master OS — automated mastery of Traditional Chinese Medicine (TCM) clinical practice — the cognitive operating system of practicing TCM physicians covering (a) 经典理论 (阴阳/五行/脏腑/气血津液/经络/六淫/七情/六经/卫气营血/三焦) classical theory, (b) 四诊 (望闻问切) clinical observation/inquiry/pulse + 八纲 (阴阳/表里/寒热/虚实) syndrome differentiation, (c) 治法 (汗吐下和温清消补 + 扶正祛邪) therapeutic principles, (d) 方剂学 (经方 时方 验方) formula science, (e) 中药学 (四气五味/归经/七情/炮制/配伍禁忌) materia medica, (f) 针灸学 (经络穴位/手法/灸法/电针) acupuncture+moxibustion, (g) 推拿按摩 tuina massage, (h) 中医各科 (内/外/妇/儿/骨伤/皮肤/眼/耳鼻喉/肛肠/老年/急症) clinical specialties, (i) 中西医结合 integrative medicine, (j) 循证中医 + 临床流行病学 evidence-based TCM + 现代研究, (k) 中医诊所备案 / 中医师资格 / NATCM 监管 / 药典 / GMP regulatory framework, (l) 国家级名老中医 学术继承人 项目 transmission program; NOT 道家修炼 / 风水 / 算命八字 (那是 玄学不是 中医), NOT 中医养生科普 (是 衍生 不是 临床主体), NOT 中医美容 (是 商业化分支), NOT 民族医学 (藏蒙维傣 是 平行体系 不在本 skill 主线 — 仅作 边界标注).: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on Traditional Chinese Medicine (TCM) clinical practice — the cognitive operating system of practicing TCM physicians covering (a) 经典理论 (阴阳/五行/脏腑/气血津液/经络/六淫/七情/六经/卫气营血/三焦) classical theory, (b) 四诊 (望闻问切) clinical observation/inquiry/pulse + 八纲 (阴阳/表里/寒热/虚实) syndrome differentiation, (c) 治法 (汗吐下和温清消补 + 扶正祛邪) therapeutic principles, (d) 方剂学 (经方 时方 验方) formula science, (e) 中药学 (四气五味/归经/七情/炮制/配伍禁忌) materia medica, (f) 针灸学 (经络穴位/手法/灸法/电针) acupuncture+moxibustion, (g) 推拿按摩 tuina massage, (h) 中医各科 (内/外/妇/儿/骨伤/皮肤/眼/耳鼻喉/肛肠/老年/急症) clinical specialties, (i) 中西医结合 integrative medicine, (j) 循证中医 + 临床流行病学 evidence-based TCM + 现代研究, (k) 中医诊所备案 / 中医师资格 / NATCM 监管 / 药典 / GMP regulatory framework, (l) 国家级名老中医 学术继承人 项目 transmission program; NOT 道家修炼 / 风水 / 算命八字 (那是 玄学不是 中医), NOT 中医养生科普 (是 衍生 不是 临床主体), NOT 中医美容 (是 商业化分支), NOT 民族医学 (藏蒙维傣 是 平行体系 不在本 skill 主线 — 仅作 边界标注). problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「中医」「中医学」「中医药」「中医诊疗」「中医临床」

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電子カルテに組み込む臨床判断支援を設計し、薬の相互作用や用量の検証、臨床スコアの計算、警告の重要度に応じた画面制御とテストを実装するスキル。

• 薬の相互作用とアレルギー確認の実装
• 患者情報に応じた用量検証
• NEWS2などの臨床スコアの実装

Stage 2 of the Clinical ASR Flywheel. Use when curating clinical terms, tagging IPA, and synthesizing a NeMo manifest. NOT for scoring (use /digital-health-clinical-asr-eval).

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Write a structured clinical case summary or case presentation. Use when asked to write a clinical case summary, case presentation, patient case report, or clinical handover. Produces a structured summary using SBAR or SOAP format. For educational and documentation purposes only — not a substitute for clinical judgement.

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Guide for annotating ENCODE regulatory variants with ClinVar clinical significance. Use when users need to check if variants in ENCODE peaks have clinical associations, find pathogenic variants in regulatory regions, or assess variant clinical impact. Trigger on: ClinVar, clinical significance, pathogenic variant, variant classification, clinical variant, disease variant, VUS, benign, likely pathogenic.

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使用 AACT (Aggregate Analysis of ClinicalTrials.gov) PostgreSQL 数据仓库进行批量、历史、聚合性临床试验数据挖掘。Use this skill when the user requests bulk SQL analysis over the full clinical trials data warehouse — historical trial trends, disease landscapes, similar-design matching, or multi-year aggregations across hundreds of thousands of NCT records. 触发场景包括：AACT 查询、临床试验批量分析、PostgreSQL 试验数据、全量 NCT 检索、试验数据挖掘、历史试验分析、clinical trials data warehouse、SQL trials、bulk trial analysis、disease landscape、试验设计相似性匹配、跨年度聚合、sponsor/phase/country 多维统计。**与 clinical-trials-v2 差异**：本 skill 走批量 SQL · 离线大数据（PostgreSQL）；v2 走实时 API · 单查询。两者互补：单条 NCT 实时状态用 v2，百万级历史挖掘用本 skill。支持云端公共 PostgreSQL（aact-db.ctti-clinicaltrials.org · 零部署）和每日 dump 本地还原（高性能 · 离线）两种连接方式，自动检测优先用本地。跨平台（macOS/Linux/Windows）参数化 SQL 防注入，read-only 强制保护。

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通过 ClinicalTrials.gov API v2 实时检索官方临床试验注册数据。触发词包括 ClinicalTrials.gov、临床试验、NCT、试验注册、招募状态、phase 1/2/3、RCT lookup、interventional trial、observational study。专注实时单次查询(≤1000 条/请求),适合"某试验最新状态/招募信息/主次要终点"场景。批量历史分析请用 aact-bulk-trials,文献检索用 pubmed-search/europepmc。

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電子カルテの診療記録、処方、検査結果画面を設計するスキル。薬の相互作用警告や変更履歴、医療現場で入力しやすい画面の設計方針を整理します。

• 診療フローを設計したいとき
• 薬の相互作用警告を実装したいとき
• 検査結果の異常値を表示したいとき

Use when planning, funding, scoping, or synthesizing enterprise research across workstreams — clinical study design, R&D program finance, market sizing/surveys, or product/user research. Triggers on "design this clinical study", "what sample size", "R&D budget", "burn rate", "capitalize or expense", "TAM SAM SOM", "market sizing", "survey design", "segment the market", "plan user interviews", "usability test", "synthesize research insights". Forks context to route to one of four Research-Operations sub-skills (clinical-research, research-finance, market-research, product-research) and returns a digest. Distinct from ra-qm-team (regulatory submission), finance (corporate close/valuation), research/grants (funding discovery), product-team (persona/journey/live experiments), and marketing-skill (campaign analytics).

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Use when designing a prospective clinical study before submission — selecting and classifying endpoints (primary / key-secondary / exploratory, with surrogate-endpoint flagging), estimating sample size and power for two-arm designs (means / proportions / survival), or scoring a study plan for feasibility and a GO / GO-WITH-CONDITIONS / REDESIGN / NO-GO phase-gate decision. Every output is an ESTIMATE plus a named human owner (clinician / biostatistician / regulatory owner) — never clinical fact, never a finished protocol. Distinct from ra-qm-team, which handles the regulatory/QM submission (ISO 13485, EU MDR, FDA 510(k)/PMA/QSR), not the study design.

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Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis.

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Use when cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing SDTM datasets, handling missing values in clinical studies, detecting outliers in lab results, or converting raw CRF data to CDISC format. Cleans and standardizes clinical trial data fo...

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Designs complete research plans that integrate clinical variables with multi-omics data from a user-provided biomedical direction. Always use this skill whenever a user wants to design, scope, or structure a study that combines clinical variables with transcriptomics, proteomics, metabolomics, epigenomics, or related omics layers for mechanism interpretation, biomarker development, risk stratification, treatment-response analysis, or translational use. It should define the clinical use case, alignment across data layers, feature-reduction and fusion logic, modeling route, mechanism-interpretation layer, validation ladder, and four workload configurations (Lite / Standard / Advanced / Publication+). Never fabricate datasets, accession numbers, sample counts, metadata completeness, platform coverage, literature references, PMIDs, DOIs, or validation status. Always include the mandatory Dataset Disclaimer immediately before any workflow section that mentions datasets or public resources.

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Extracts concrete unmet clinical needs from guidelines, reviews, real-world studies, and clinical-practice evidence. Use this skill when a user wants to turn broad medical research value into specific clinical pain points such as weak early detection, poor risk stratification, treatment-response heterogeneity, monitoring gaps, diagnostic delay, undertreatment, overtreatment, or implementation failure. Always ground unmet-need claims in retrieved evidence and distinguish true care gaps from generic statements of importance.

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Organizes the evidence and competitive landscape around a drug, target, or pathway by separating disease relevance, tractability, preclinical evidence, clinical evidence, modality fit, and crowding. Always map what is biologically supported, what is druggable, what has actually advanced, and what remains strategically open. Never confuse target relevance with druggability, preclinical activity with clinical promise, or narrative excitement with validated development maturity. Never fabricate references, trial status, approval status, company activity, or asset metadata.

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Draft a clinical trial protocol synopsis with the elements regulators and IRBs expect. Use when asked to write a clinical trial protocol, a study protocol synopsis, a trial design, or to structure endpoints/eligibility/statistics for an interventional study. Produces a structured protocol synopsis — objectives, design, population with eligibility, interventions, endpoints, statistics, and safety/ethics — for expert review. (For non-clinical/UX research, use research-protocol.)

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Generate clinical trial protocols for medical devices or drugs. This skill should be used when users say "Create a clinical trial protocol", "Generate protocol for [device/drug]", "Help me design a clinical study", "Research similar trials for [intervention]", or when developing FDA submission documentation for investigational products.

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Generate clinical trial protocols for medical devices or drugs. This skill should be used when users say "Create a clinical trial protocol", "Generate protocol for [device/drug]", "Help me design a clinical study", "Research similar trials for [intervention]", or when developing FDA submission documentation for investigational products.

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Guides DPIA for health and medical data processing covering Art. 9(2)(h)-(j) exemptions, HIPAA crosswalk for transatlantic operations, clinical trial data protection under EU CTR 536/2014, and genetic data specifics under Art. 9(1). Activate for healthcare systems, clinical research, health apps, or medical device data. Keywords: health data, DPIA, Art. 9, clinical trial, genetic data, HIPAA, medical records, special category.

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Think and work like an expert Clinical Embryologist. Use when a task calls for Clinical Embryologist judgment. Reasons from gamete and embryo biology, manufacturing-quality lab control, and prespecified cycle/oocyte/embryo denominators through Vienna consensus KPIs, Gardner/ASEBIR grading, time-lapse morphokinetics, WHO 6th-edition andrology, and vitrification SOPs while treating media-lot and incubator-gas drift, witness mix-ups, abnormal fertilization (1PN/3PN), and clinical case-mix confounding as first-class failure modes.

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Think and work like an expert Clinical Pharmacologist. Use when a task calls for Clinical Pharmacologist judgment. Reasons from exposure–response, popPK (NONMEM), DDI (ICH M12), TDM/NTI windows, and renal/hepatic/allometric adjustment; aligns dose finding with ICH E4 and FDA clinical pharmacology labeling.

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Think and work like an expert Clinical Epidemiologist. Use when a task calls for Clinical Epidemiologist judgment. Clinical epidemiology expert for causal study design, observational bias control, GRADE/EBM synthesis, and principled reporting (CONSORT/STROBE/PRISMA).

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临床诊断思维 (临床诊断思维 / 临床推理 (Clinical Diagnostic Reasoning) — 医生『怎么想病』的元学科:从症状/体征/检查到诊断结论的认知操作系统,从业者(临床医生/住院医/规培生,尤其全科/急诊/大内科等以未分化主诉为生的科室)、医学生与医学教育者、诊断安全与质量改进研究者、以及做医疗 AI 辅助诊断产品的人的视角。覆盖: (a) 第一性张力 — **直觉模式识别 (System 1: illness scripts 疾病脚本 / pattern recognition / gestalt, 『资深人一眼认出 aunt Minnie』) ⇄ 分析性推理 (System 2: hypothetico-deductive 假设演绎 / Bayesian 概率更新)** 的 dual-process 双过程理论 (Kahneman→Croskerry/Norman 谱系), 资深≠更会分析而是『脚本库更大+校准更好』; 更深层论战 — **『认知去偏可教 (Croskerry: bias awareness / cognitive forcing strategies / diagnostic timeout) ⇄ 偏倚标签是马后炮、知识结构才是主因 (Norman/Sherbino/Monteiro: debiasing 干预 transfer 证据弱, bias 是 hindsight 标签)』** — 本行最核心学术对垒; 『概率思维 (验前概率 × 似然比 → 验后概率, Pauker-Kassirer test/treatment threshold 阈值模型) ⇄ 穷尽式排查 (rule-out everything / 防御性医疗 / VOMIT)』; 『临床 gestalt ⇄ 结构化临床决策规则 (Wells/PERC/HEART)』; 『诊断简约 Occam's razor ⇄ Hickam's dictum (病人可以同时得 N 个病)』; 『床旁体格检查复兴 (Verghese Stanford 25 / McGee 循证体检) ⇄ 影像检验替代床旁』; (b) 方法论正典 — illness script theory (Schmidt/Boshuizen: enabling conditions/fault/consequences 三段结构), problem representation + semantic qualifiers 问题表征与语义限定词 (Bowen NEJM 2006, 把病人翻成 one-liner), hypothetico-deductive model (Elstein 1978《Medical Problem Solving》: 早期假设生成+定向检验), Bayesian 工具箱 (sensitivity/specificity/LR/Fagan nomogram/SnNout-SpPin), threshold model (Pauker-Kassirer NEJM 1975/1980), 认知偏倚分类学 (anchoring/premature closure/availability/confirmation/base-rate neglect/search satisficing/diagnostic momentum — Croskerry 偏倚清单), 去偏与元认知策略 (diagnostic timeout/cognitive forcing/Ely checklist/calibration), schema-based reasoning (Clinical Problem Solvers schemas: 按 pivot 症状走分支), reflective practice 结构化反思 (Mamede/Schmidt), 诊断错误科学 (NAM 2015 报告定义 / Newman-Toker Big Three / Hardeep Singh e-triggers / SAFER Dx 框架); (c) 行业结构与角色 — 医学生→实习/住院医 (晨会 morning report/查房被 pimping/汇报训练)→主治 attending→master clinician (NEJM CPC discussant/晨会大师如 Dhaliwal); 配套生态: 医学教育者 (clinical reasoning curriculum + 评估: script concordance test/key features exam/OSCE), 诊断安全研究者 (SIDM/AHRQ), 诊断辅助与 CDS 工具开发者 (DDx generator/AI); (d) 核心工作流 — 数据采集 (病史为王 + 循证体检 + 针对性检验影像) → 问题表征 (one-liner + semantic qualifiers) → 鉴别诊断生成 (schema / 解剖定位法 / VINDICATE-M 病因筛, 按『常见可能 × 致命不能漏 can't-miss』双轴排序) → 假设定向检验 (按 LR 选检查 / 阈值决策) → working diagnosis + 安全网 (red flags 交代 / test of time / test of treatment) → 反馈校准 (follow-up / M&M / diagnostic timeout); 教学工作流: 晨会渐进披露汇报 / CPC / SNAPPS / one-minute preceptor / virtual morning report; AI 增强工作流 (2023-2026: LLM 鉴别诊断头脑风暴 / OpenEvidence 检索 / ambient scribe 释放认知带宽 + 自动化偏倚 guardrails); (e) 产出物 — one-liner, problem list, prioritized DDx, assessment & plan (按问题分层), 晨会/CPC 汇报, M&M 复盘, 诊断不确定性沟通 (『最可能是 X, 但出现 Y 红旗立刻回来』); (f) 教育与评估 — script concordance test / key feature exam / EPA / 里程碑; 中国语境: 人卫《诊断学》教材 / 执业医师考试 / 规培结业临床思维考核; (g) 争议/批判 — debiasing 之争 (Croskerry vs Norman-Sherbino『knowledge is the cure』, 去偏 RCT 效果弱 / bias 标签不可证伪), 诊断错误率数字之争 (『10-15%』经典估计 / Newman-Toker 79.5 万美国年严重伤害外推方法被质疑 / 尸检符合率), dual-process 二分被批过度简化 (连续谱 / 难以实证分离), 决策规则 vs gestalt (资深 gestalt 常不输 Wells 类规则 / 算法厌恶), vignette 研究外推性 + context/case specificity (推理高度内容绑定不可通用迁移 [Norman/Eva] — 对『教推理通用课』产业的根本批判), AI 辅助诊断 2023-2026 (GPT-4 在 NEJM CPC/vignette 追平或超医生 [Kanjee 2023 JAMA / Goh 2024 JAMA Netw Open『AI alone > physician+AI』悖论] / Google AMIE / OpenEvidence 爆发 vs 自动化偏倚 / 去技能化 / 真实环境验证缺失), 防御性医疗与过度检查 (incidentaloma 瀑布 / VOMIT), 体检衰亡之争, pimping 教学法争议; (h) 流派/思想谱系 — 决策科学/Bayesian 派 (Ledley-Lusted 1959 → Elstein/Kassirer/Sox/Pauker/Eddy → Brush) vs 认知心理/双过程派 (Kahneman-Tversky → Croskerry/Graber) vs 教育认知派 (Schmidt/Boshuizen illness scripts → Norman/Eva/Mamede/Durning 情境认知) vs 诊断安全/系统派 (NAM 2015 / Newman-Toker / Hardeep Singh / Graber-SIDM: 错误=系统×认知共因) vs 床旁临床派 (Osler 传统 → Tierney aphorisms / Verghese / McGee / Dhaliwal / Saint: 病史体检为王 + 刻意练习) vs AI/计算派 (INTERNIST-1/DXplain 专家系统 → Isabel → LLM 世代 Rodman/Topol)。不含: 具体专科治疗方案与手术决策深度 (足踝外科/种植牙等另有 skill), 中医辨证 (另有 skill), EBM 文献批判性评价方法学 (相关但本 skill 聚焦诊断推理本身), 护理诊断体系 (NANDA), 影像/病理知觉型读片训练 (相邻但独立), 精神科 DSM 结构化访谈细节, 医患沟通技巧全集 (只覆盖诊断不确定性沟通), 患者自查/自我诊断指

日本語の概要は準備中です。原文の説明を表示しています。

Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis.

日本語の概要は準備中です。原文の説明を表示しています。