domain-research-health-science

Compare original and translation side by side

🇺🇸

Original

English

🇨🇳

Translation

Chinese

Domain Research: Health Science

Domain Research：健康科学

Purpose

目的

This skill helps structure clinical and health science research using evidence-based medicine frameworks. It guides you through formulating precise research questions (PICOT), evaluating study quality (hierarchy of evidence, bias assessment, GRADE), prioritizing outcomes (patient-important vs surrogate), and synthesizing evidence for clinical decision-making.

该技能借助循证医学框架帮助结构化临床与健康科学研究。它将引导你完成精准研究问题的制定（PICOT）、研究质量评估（证据层级、偏倚评估、GRADE）、结局优先排序（患者重要结局vs替代结局），以及为临床决策合成证据的全流程。

When to Use

适用场景

Use this skill when:

Formulating research questions: Structuring clinical questions using P ICO T (Population, Intervention, Comparator, Outcome, Timeframe)
Evaluating evidence quality: Assessing study design strength, risk of bias, certainty of evidence (GRADE framework)
Prioritizing outcomes: Distinguishing patient-important outcomes from surrogate endpoints, creating outcome hierarchies
Systematic reviews: Planning or conducting systematic reviews, meta-analyses, or evidence syntheses
Clinical guidelines: Creating evidence summaries for practice guidelines or decision support
Trial design: Designing RCTs, pragmatic trials, or observational studies with rigorous methodology
Regulatory submissions: Preparing evidence dossiers for drug/device approval or reimbursement decisions
Critical appraisal: Evaluating published research for clinical applicability and methodological quality

Trigger phrases: "clinical trial design", "systematic review", "PICOT question", "evidence quality", "bias assessment", "GRADE", "outcome measures", "research protocol", "evidence synthesis", "study appraisal"

在以下场景中使用该技能：

制定研究问题：使用PICOT（Population、Intervention、Comparator、Outcome、Timeframe）框架结构化临床问题
评估证据质量：评估研究设计强度、偏倚风险、证据确定性（GRADE框架）
优先排序结局：区分患者重要结局与替代终点，建立结局层级
系统评价：规划或开展systematic reviews、meta-analyses或证据合成
临床指南：为实践指南或决策支持编写证据摘要
试验设计：采用严谨方法设计RCT、实用性试验或观察性研究
监管申报：为药物/器械获批或报销决策准备证据 dossier
批判性评价：评估已发表研究的临床适用性与方法学质量

触发短语："clinical trial design", "systematic review", "PICOT question", "evidence quality", "bias assessment", "GRADE", "outcome measures", "research protocol", "evidence synthesis", "study appraisal"

What Is It?

什么是Domain Research？

Domain Research: Health Science applies structured frameworks from evidence-based medicine to ensure clinical research is well-formulated, methodologically sound, and clinically meaningful.

Quick example:

Vague question: "Does this drug work for heart disease?"

PICOT-structured question:

P (Population): Adults >65 with heart failure and reduced ejection fraction
I (Intervention): SGLT2 inhibitor (dapagliflozin 10mg daily)
C (Comparator): Standard care (ACE inhibitor + beta-blocker)
O (Outcome): All-cause mortality (primary); hospitalizations, quality of life (secondary)
T (Timeframe): 12-month follow-up

Result: Precise, answerable research question that guides study design, literature search, and outcome selection.

Domain Research: Health Science 应用循证医学的结构化框架，确保临床研究制定合理、方法严谨且具有临床意义。

快速示例：

模糊问题："这款药物对心脏病有效吗？"

PICOT结构化问题：

P（研究人群）：65岁以上患有射血分数降低型心力衰竭的成年人
I（干预措施）：SGLT2抑制剂（达格列净10mg/日）
C（对照措施）：标准治疗（ACE抑制剂+β受体阻滞剂）
O（结局指标）：全因死亡率（主要结局）；住院率、生活质量（次要结局）
T（时间范围）：12个月随访期

结果：精准、可回答的研究问题，为研究设计、文献检索和结局指标选择提供指导。

Workflow

工作流程

Copy this checklist and track your progress:

Health Research Progress:
- [ ] Step 1: Formulate research question (PICOT)
- [ ] Step 2: Assess evidence hierarchy and study design
- [ ] Step 3: Evaluate study quality and bias
- [ ] Step 4: Prioritize and define outcomes
- [ ] Step 5: Synthesize evidence and grade certainty
- [ ] Step 6: Create decision-ready summary

Step 1: Formulate research question (PICOT)

Use PICOT framework to structure answerable clinical question. Define Population (demographics, condition, setting), Intervention (treatment, exposure, diagnostic test), Comparator (alternative treatment, placebo, standard care), Outcome (patient-important endpoints), and Timeframe (follow-up duration). See resources/template.md for structured templates.

Step 2: Assess evidence hierarchy and study design

Determine appropriate study design based on research question type (therapy: RCT; diagnosis: cross-sectional; prognosis: cohort; harm: case-control or cohort). Understand hierarchy of evidence (systematic reviews > RCTs > cohort > case-control > case series). See resources/methodology.md for design selection guidance.

Step 3: Evaluate study quality and bias

Apply risk of bias assessment tools (Cochrane RoB 2 for RCTs, ROBINS-I for observational studies, QUADAS-2 for diagnostic accuracy). Evaluate randomization, blinding, allocation concealment, incomplete outcome data, selective reporting. See resources/methodology.md for detailed criteria.

Step 4: Prioritize and define outcomes

Distinguish patient-important outcomes (mortality, symptoms, quality of life, function) from surrogate endpoints (biomarkers, lab values). Create outcome hierarchy: critical (decision-driving), important (informs decision), not important. Define measurement instruments and minimal clinically important differences (MCID). See resources/template.md for prioritization framework.

Step 5: Synthesize evidence and grade certainty

Apply GRADE (Grading of Recommendations Assessment, Development and Evaluation) to rate certainty of evidence (high, moderate, low, very low). Consider study limitations, inconsistency, indirectness, imprecision, publication bias. Upgrade for large effects, dose-response, or confounders reducing effect. See resources/methodology.md for rating guidance.

Step 6: Create decision-ready summary

Produce evidence profile or summary of findings table linking outcomes to certainty ratings and effect estimates. Include clinical interpretation, applicability assessment, and evidence gaps. Validate using resources/evaluators/rubric_domain_research_health_science.json. Minimum standard: Average score ≥ 3.5.

复制以下清单并跟踪进度：

Health Research Progress:
- [ ] Step 1: Formulate research question (PICOT)
- [ ] Step 2: Assess evidence hierarchy and study design
- [ ] Step 3: Evaluate study quality and bias
- [ ] Step 4: Prioritize and define outcomes
- [ ] Step 5: Synthesize evidence and grade certainty
- [ ] Step 6: Create decision-ready summary

步骤1：制定研究问题（PICOT）

使用PICOT框架构建可回答的临床问题。明确研究人群（人口统计学特征、疾病、场景）、干预措施（治疗、暴露、诊断测试）、对照措施（替代治疗、安慰剂、标准治疗）、结局指标（患者重要终点）和时间范围（随访时长）。结构化模板请参见resources/template.md。

步骤2：评估证据层级与研究设计

根据研究问题类型确定合适的研究设计（治疗类：RCT；诊断类：横断面研究；预后类：队列研究；伤害类：病例对照研究或队列研究）。了解证据层级（systematic reviews > RCTs > 队列研究 > 病例对照研究 > 病例系列）。研究设计选择指南请参见resources/methodology.md。

步骤3：评估研究质量与偏倚

应用经过验证的偏倚风险评估工具（RCT采用Cochrane RoB 2，观察性研究采用ROBINS-I，诊断准确性研究采用QUADAS-2）。评估随机化、盲法、分配隐藏、不完整结局数据、选择性报告等情况。详细评估标准请参见resources/methodology.md。

步骤4：优先排序与定义结局

区分患者重要结局（死亡率、症状、生活质量、功能）与替代终点（生物标志物、实验室数值）。建立结局层级：关键结局（驱动决策）、重要结局（辅助决策）、非重要结局。定义测量工具与最小临床重要差异（MCID）。优先排序框架请参见resources/template.md。

步骤5：合成证据与分级确定性

应用GRADE（推荐分级的评估、制定与评价）对证据确定性进行评级（高、中、低、极低）。考虑研究局限性、不一致性、间接性、不精确性、发表偏倚。若存在大效应量、剂量反应关系或混杂因素降低效应，可升级评级。评级指南请参见resources/methodology.md。

步骤6：制定可用于决策的摘要

生成证据概况或研究结果摘要表，将结局指标与确定性评级、效应估计值关联。包含临床解读、适用性评估与证据缺口。使用resources/evaluators/rubric_domain_research_health_science.json进行验证。最低标准：平均分≥3.5。

Common Patterns

常见模式

Pattern 1: Therapy/Intervention Question

PICOT: Adults with condition → new treatment vs standard care → patient-important outcomes → follow-up period
Study design: RCT preferred (highest quality for causation); systematic review of RCTs for synthesis
Key outcomes: Mortality, morbidity, quality of life, adverse events
Bias assessment: Cochrane RoB 2 (randomization, blinding, attrition, selective reporting)
Example: SGLT2 inhibitors for heart failure → reduced mortality (GRADE: high certainty)

Pattern 2: Diagnostic Test Accuracy

PICOT: Patients with suspected condition → new test vs reference standard → sensitivity/specificity → cross-sectional
Study design: Cross-sectional study with consecutive enrollment; avoid case-control (inflates accuracy)
Key outcomes: Sensitivity, specificity, positive/negative predictive values, likelihood ratios
Bias assessment: QUADAS-2 (patient selection, index test, reference standard, flow and timing)
Example: High-sensitivity troponin for MI → sensitivity 95%, specificity 92% (GRADE: moderate certainty)

Pattern 3: Prognosis/Risk Prediction

PICOT: Population with condition/exposure → risk factors → outcomes (death, disease progression) → long-term follow-up
Study design: Prospective cohort (follow from exposure to outcome); avoid retrospective (recall bias)
Key outcomes: Incidence, hazard ratios, absolute risk, risk prediction model performance (C-statistic, calibration)
Bias assessment: ROBINS-I or PROBAST (for prediction models)
Example: Framingham Risk Score for CVD → C-statistic 0.76 (moderate discrimination)

Pattern 4: Harm/Safety Assessment

PICOT: Population exposed to intervention → adverse events → timeframe for rare/delayed harms
Study design: RCT for common harms; observational (cohort, case-control) for rare harms (larger sample, longer follow-up)
Key outcomes: Serious adverse events, discontinuations, organ-specific toxicity, long-term safety
Bias assessment: Different for rare vs common harms; consider confounding by indication in observational studies
Example: NSAID cardiovascular risk → observational studies show increased MI risk (GRADE: low certainty due to confounding)

Pattern 5: Systematic Review/Meta-Analysis

PICOT: Defined in protocol; guides search strategy, inclusion criteria, outcome extraction
Study design: Comprehensive search, explicit eligibility criteria, duplicate screening/extraction, bias assessment, quantitative synthesis (if appropriate)
Key outcomes: Pooled effect estimates (RR, OR, MD, SMD), heterogeneity (I²), certainty rating (GRADE)
Bias assessment: Individual study RoB + review-level assessment (AMSTAR 2 for review quality)
Example: Statins for primary prevention → RR 0.75 for MI (95% CI 0.70-0.80, I²=12%, GRADE: high certainty)

模式1：治疗/干预问题

PICOT：患有疾病的成年人 → 新治疗vs标准治疗 → 患者重要结局 → 随访期
研究设计：优先选择RCT（因果推断的最高质量证据）；采用systematic reviews of RCTs进行证据合成
关键结局：死亡率、发病率、生活质量、不良事件
偏倚评估：Cochrane RoB 2（随机化、盲法、失访、选择性报告）
示例：SGLT2抑制剂治疗心力衰竭 → 降低死亡率（GRADE：高确定性）

模式2：诊断测试准确性

PICOT：疑似患有疾病的患者 → 新测试vs金标准 → 灵敏度/特异度 → 横断面研究
研究设计：连续入组的横断面研究；避免使用病例对照研究（会高估准确性）
关键结局：灵敏度、特异度、阳性/阴性预测值、似然比
偏倚评估：QUADAS-2（患者选择、指数测试、金标准、流程与时间）
示例：高敏肌钙蛋白检测心肌梗死 → 灵敏度95%，特异度92%（GRADE：中确定性）

模式3：预后/风险预测

PICOT：患有疾病/暴露的人群 → 风险因素 → 结局（死亡、疾病进展） → 长期随访
研究设计：前瞻性队列研究（从暴露追踪至结局）；避免回顾性研究（回忆偏倚）
关键结局：发病率、风险比、绝对风险、风险预测模型性能（C统计量、校准度）
偏倚评估：ROBINS-I或PROBAST（针对预测模型）
示例：Framingham风险评分预测心血管疾病 → C统计量0.76（中等区分度）

模式4：伤害/安全性评估

PICOT：暴露于干预措施的人群 → 不良事件 → 罕见/延迟伤害的时间范围
研究设计：RCT评估常见伤害；观察性研究（队列、病例对照）评估罕见伤害（样本量更大、随访时间更长）
关键结局：严重不良事件、停药率、器官特异性毒性、长期安全性
偏倚评估：罕见伤害与常见伤害的评估方法不同；考虑观察性研究中的指征混杂
示例：NSAIDs的心血管风险 → 观察性研究显示心肌梗死风险增加（GRADE：低确定性，因混杂因素）

模式5：Systematic Reviews/Meta-Analysis

PICOT：在研究方案中明确；指导检索策略、纳入标准、结局提取
研究设计：全面检索、明确的纳入标准、重复筛选/提取、偏倚评估、定量合成（如适用）
关键结局：合并效应估计值（RR、OR、MD、SMD）、异质性（I²）、确定性评级（GRADE）
偏倚评估：单个研究的偏倚风险+综述层面评估（AMSTAR 2评估综述质量）
示例：他汀类药物用于一级预防 → 心肌梗死RR 0.75（95% CI 0.70-0.80，I²=12%，GRADE：高确定性）

Guardrails

注意事项

Critical requirements:

Use PICOT for all clinical questions: Vague questions lead to unfocused research. Always specify Population, Intervention, Comparator, Outcome, Timeframe explicitly. Avoid "does X work?" without defining for whom, compared to what, and measuring which outcomes.
Match study design to question type: RCTs answer therapy questions (causal inference). Cohort studies answer prognosis. Cross-sectional studies answer diagnosis. Case-control studies answer rare harm or etiology. Don't claim causation from observational data or use case series for treatment effects.
Prioritize patient-important outcomes over surrogates: Surrogate endpoints (biomarkers, lab values) don't always correlate with patient outcomes. Focus on mortality, morbidity, symptoms, function, quality of life. Only use surrogates if validated relationship to patient outcomes exists.
Assess bias systematically, not informally: Use validated tools (Cochrane RoB 2, ROBINS-I, QUADAS-2) not subjective judgment. Bias assessment affects certainty of evidence and clinical recommendations. Common biases: selection bias, performance bias (lack of blinding), detection bias, attrition bias, reporting bias.
Apply GRADE to rate certainty of evidence: Don't conflate study design with certainty. RCTs start as high certainty but can be downgraded (serious limitations, inconsistency, indirectness, imprecision, publication bias). Observational studies start as low but can be upgraded (large effect, dose-response, residual confounding reducing effect).
Distinguish statistical significance from clinical importance: p < 0.05 doesn't mean clinically meaningful. Consider minimal clinically important difference (MCID), absolute risk reduction, number needed to treat (NNT). Small p-value with tiny effect size is statistically significant but clinically irrelevant.
Assess external validity and applicability: Evidence from selected trial populations may not apply to your patient. Consider PICO match (are your patients similar?), setting differences (tertiary center vs community), intervention feasibility, patient values and preferences.
State limitations and certainty explicitly: All evidence has limitations. Specify what's uncertain, where evidence gaps exist, and how this affects confidence in recommendations. Avoid overconfident claims not supported by evidence quality.

Common pitfalls:

❌ Treating all RCTs as high quality: RCTs can have serious bias (inadequate randomization, unblinded, high attrition). Always assess bias.
❌ Ignoring heterogeneity in meta-analysis: High I² (>50%) suggests important differences across studies. Explore sources (population, intervention, outcome definition) before pooling.
❌ Confusing association with causation: Observational studies show association, not causation. Residual confounding is always possible.
❌ Using composite outcomes uncritically: Composite endpoints (e.g., "death or MI or hospitalization") obscure which component drives effect. Report components separately.
❌ Accepting industry-funded evidence uncritically: Pharmaceutical/device company-sponsored trials may have bias (outcome selection, selective reporting). Assess for conflicts of interest.
❌ Over-interpreting subgroup analyses: Most subgroup effects are chance findings. Only credible if pre-specified, statistically tested for interaction, and biologically plausible.

关键要求：

所有临床问题均使用PICOT框架：模糊问题会导致研究重点不明确。必须明确指定研究人群、干预措施、对照措施、结局指标和时间范围。避免使用“X是否有效？”这类未明确适用人群、对照措施和结局指标的问题。
研究设计与问题类型匹配：RCT用于回答治疗问题（因果推断）。队列研究用于回答预后问题。横断面研究用于回答诊断问题。病例对照研究用于回答罕见伤害或病因问题。不要从观察性数据中推断因果关系，也不要用病例系列来评估治疗效果。
优先考虑患者重要结局而非替代终点：替代终点（生物标志物、实验室数值）并不总是与患者结局相关。聚焦于死亡率、发病率、症状、功能、生活质量。仅当替代终点与患者结局的关联已得到验证时，才可使用。
系统评估偏倚，而非主观判断：使用经过验证的工具（Cochrane RoB 2、ROBINS-I、QUADAS-2），而非主观判断。偏倚评估会影响证据确定性和临床推荐。常见偏倚：选择偏倚、执行偏倚（缺乏盲法）、检测偏倚、失访偏倚、报告偏倚。
应用GRADE对证据确定性进行评级：不要将研究设计与确定性混淆。RCT初始为高确定性，但可能因严重局限性、不一致性、间接性、不精确性、发表偏倚而降级。观察性研究初始为低确定性，但可能因大效应量、剂量反应关系或残留混杂因素降低效应而升级。
区分统计学显著性与临床重要性：p<0.05并不意味着具有临床意义。考虑最小临床重要差异（MCID）、绝对风险降低值、需治疗人数（NNT）。小p值但效应量微小的结果具有统计学显著性，但临床意义不大。
评估外部有效性与适用性：特定试验人群的证据可能不适用于你的患者。考虑PICO匹配度（你的患者是否相似？）、场景差异（三级中心vs社区）、干预措施可行性、患者价值观与偏好。
明确说明局限性与确定性：所有证据都有局限性。明确说明不确定性所在、证据缺口以及这些如何影响推荐的可信度。避免做出无证据质量支持的过度自信声明。

常见误区：

❌ 认为所有RCT都是高质量的：RCT可能存在严重偏倚（随机化不充分、未盲法、高失访率）。始终评估偏倚。
❌ 忽略meta分析中的异质性：高I²（>50%）表明研究间存在重要差异。合并前需探索异质性来源（人群、干预措施、结局定义）。
❌ 混淆关联与因果关系：观察性研究显示关联，而非因果关系。残留混杂因素始终存在。
❌ 不加批判地使用复合结局：复合终点（如“死亡或心肌梗死或住院”）会掩盖哪个组分驱动效应。单独报告各组分。
❌ 不加批判地接受行业资助的证据：制药/器械公司赞助的试验可能存在偏倚（结局选择、选择性报告）。评估利益冲突。
❌ 过度解读亚组分析：大多数亚组效应是偶然发现的。仅当亚组分析是预先指定、经过交互作用统计检验且具有生物学合理性时，才具有可信度。

Quick Reference

快速参考

Key resources:

resources/template.md: PICOT framework, outcome hierarchy template, evidence table, GRADE summary template
resources/methodology.md: Evidence hierarchy, bias assessment tools, GRADE detailed guidance, study design selection, systematic review methods
resources/evaluators/rubric_domain_research_health_science.json: Quality criteria for research questions, evidence synthesis, and clinical interpretation

PICOT Template:

P (Population): [Who? Age, sex, condition, severity, setting]
I (Intervention): [What? Drug, procedure, test, exposure - dose, duration, route]
C (Comparator): [Compared to what? Placebo, standard care, alternative treatment]
O (Outcome): [What matters? Mortality, symptoms, QoL, harms - measurement instrument, timepoint]
T (Timeframe): [How long? Follow-up duration, time to outcome]

Evidence Hierarchy (Therapy Questions):

Systematic reviews/meta-analyses of RCTs
Individual RCTs (large, well-designed)
Cohort studies (prospective)
Case-control studies
Case series, case reports
Expert opinion, pathophysiologic rationale

GRADE Certainty Ratings:

High (⊕⊕⊕⊕): Very confident true effect is close to estimated effect
Moderate (⊕⊕⊕○): Moderately confident, true effect likely close but could be substantially different
Low (⊕⊕○○): Limited confidence, true effect may be substantially different
Very Low (⊕○○○): Very little confidence, true effect likely substantially different

Typical workflow time:

PICOT formulation: 10-15 minutes
Single study critical appraisal: 20-30 minutes
Systematic review protocol: 2-4 hours
Evidence synthesis with GRADE: 1-2 hours
Full systematic review: 40-100 hours (depending on scope)

When to escalate:

Complex statistical meta-analysis (network meta-analysis, IPD meta-analysis)
Advanced causal inference methods (instrumental variables, propensity scores)
Health technology assessment (cost-effectiveness, budget impact)
Guideline development panels (requires multi-stakeholder consensus) → Consult biostatistician, health economist, or guideline methodologist

Inputs required:

Research question (clinical scenario or decision problem)
Evidence sources (studies to appraise, databases for systematic review)
Outcome preferences (which outcomes matter most to patients/clinicians)
Context (setting, patient population, decision urgency)

Outputs produced:

```
domain-research-health-science.md
```
: Structured research question, evidence appraisal, outcome hierarchy, certainty assessment, clinical interpretation

关键资源：

resources/template.md：PICOT框架、结局层级模板、证据表、GRADE摘要模板
resources/methodology.md：证据层级、偏倚评估工具、GRADE详细指南、研究设计选择、systematic reviews方法
resources/evaluators/rubric_domain_research_health_science.json：研究问题、证据合成与临床解读的质量标准

PICOT模板：

P（研究人群）：[谁？年龄、性别、疾病、严重程度、场景]
I（干预措施）：[什么？药物、操作、测试、暴露 - 剂量、时长、途径]
C（对照措施）：[与什么对比？安慰剂、标准治疗、替代治疗]
O（结局指标）：[什么最重要？死亡率、症状、生活质量、伤害 - 测量工具、时间点]
T（时间范围）：[多久？随访时长、结局出现时间]

证据层级（治疗问题）：

systematic reviews/meta-analyses of RCTs
单个RCT（大样本、设计良好）
队列研究（前瞻性）
病例对照研究
病例系列、病例报告
专家意见、病理生理学原理

GRADE确定性评级：

高（⊕⊕⊕⊕）：非常确信真实效应接近估计效应
中（⊕⊕⊕○）：中等确信，真实效应可能接近估计效应，但也可能存在较大差异
低（⊕⊕○○）：信心有限，真实效应可能与估计效应存在较大差异
极低（⊕○○○）：信心极低，真实效应很可能与估计效应存在较大差异

典型工作流程时长：

PICOT问题制定：10-15分钟
单个研究批判性评价：20-30分钟
systematic reviews方案：2-4小时
采用GRADE的证据合成：1-2小时
完整systematic reviews：40-100小时（取决于范围）

需升级咨询的场景：

复杂统计meta分析（网络meta分析、IPD meta分析）
高级因果推断方法（工具变量、倾向评分）
卫生技术评估（成本效益、预算影响）
指南制定小组（需要多利益相关方共识） → 咨询生物统计学家、卫生经济学家或指南方法学家

所需输入：

研究问题（临床场景或决策问题）
证据来源（待评价的研究、systematic reviews的数据库）
结局偏好（对患者/临床医生最重要的结局指标）
场景信息（医疗场景、患者人群、决策紧迫性）

生成的输出：

```
domain-research-health-science.md
```
：结构化研究问题、证据评价、结局层级、确定性评估、临床解读