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临床研究评估与证据追溯

为汇总层面的临床研究整理证据、评价、模型和治理材料,提供 GRADE 资料与逻辑追溯检查;适用研究用途。

开源精选医疗业务31 个文件50,135 Stars浏览次数 0K-Dense-AI/scientific-agent-skills MIT版本 a1043ef
开始使用

适用环境:Python 3.11+; local files only; bundled scripts use the standard library and require no network, credentials, API keys, LLMs, or image services.

输入资料
汇总或原文允许的研究材料 预先明确的研究问题与评价目标 阅读本节完整说明
交付内容
研究评价与证据资料 来源、限制与人工复核清单 阅读本节完整说明
  1. 获取完整包,保留配套文件的目录结构。
  2. 按照原文准备所需工具、依赖与输入资料。
  3. 阅读对应使用步骤与输入输出说明,在自己的环境中验证结果。

使用提示摘自原文,配套文件保留来源版本;实际运行条件以各包说明为准。

正文

获取原文件

references/evidence_profiles.md

Purpose

An evidence profile transparently records a human panel's judgments about a body of evidence for each important outcome. It is not an article-scoring shortcut and does not produce a patient-care recommendation.

Use the current GRADE Book and the GRADE Working Group as the controlling methodology. The GRADE Book is replacing the older handbook with progressively updated content.

Non-Automation Rule

Never:

  • infer certainty from keywords, abstracts, p-values, journal name, or study design alone;
  • count checklist items to calculate certainty;
  • treat one study's risk-of-bias judgment as certainty in a body of evidence;
  • equate certainty with recommendation strength;
  • assign recommendation strength without an Evidence-to-Decision process and a responsible panel;
  • invent source citations or downgrade/upgrade rationales.

The bundled checker verifies structure, allowed labels, human attribution, rationale, and source linkage. It does not alter or endorse a judgment.

Unit of Assessment

Rate certainty separately for every critical or important outcome. Include desirable and undesirable effects. Different outcomes may have different:

  • bodies of evidence;
  • risk-of-bias concerns;
  • directness;
  • precision;
  • reporting bias;
  • certainty.

Do not collapse all outcomes into a single study-level grade.

Required Profile Fields

Question

  • Population
  • Intervention/exposure/index approach
  • Comparator/reference
  • Outcomes and time horizons
  • Setting and decision context

Sources

For every source include:

  • stable source ID;
  • full citation;
  • URL or DOI;
  • publication type;
  • version/date;
  • access date when content is living.

Effect

For each outcome record:

  • measure and direction;
  • absolute and relative effects when appropriate;
  • confidence or credible interval;
  • participants and studies;
  • follow-up/horizon;
  • missingness;
  • whether the estimate is adjusted;
  • applicability limits.

Do not convert an effect into a clinical instruction.

Certainty Domains

Every domain entry requires a judgment, rationale, source IDs, and human reviewer role.

Risk of Bias

Use a design-appropriate tool. Describe how limitations could change the estimated effect. Do not use a numeric quality score as a substitute.

Inconsistency

Examine the direction and magnitude of effects, interval overlap, heterogeneity, and plausible explanations. A statistical heterogeneity value alone is not the judgment.

Indirectness

Compare population, intervention/exposure, comparator, outcome, time horizon, setting, and evidence pathway with the framed question.

Imprecision

Use the current GRADE Book imprecision chapter: record the target of certainty, prespecified thresholds, and absolute-effect intervals where appropriate. Its human judgments can include extremely_serious concerns (three levels); the checker accepts that label for imprecision without deriving the final certainty. Do not import universal event counts, invent clinical cut points, or count a shared concern twice across domains.

Dissemination Bias (Publication Bias)

The current GRADE Book uses dissemination bias to cover selective availability of evidence, including unpublished and obscurely published results. Consider missing studies/results, small-study effects, time/language effects, sponsorship, registrations, and protocols. Separate selective results within a study from missing evidence across the body of evidence and document overlap with risk of bias. The JSON key remains publication_bias for compatibility; record the broader assessment in its rationale.

Upgrading Considerations

When the selected GRADE approach permits, a panel may consider large effects, dose-response gradients, or plausible residual confounding. Each requires explicit methodology, rationale, and citations. “Statistically significant” is not an upgrading reason.

Final Certainty

Allowed labels:

  • high;
  • moderate;
  • low;
  • very low.

Record:

  • the final human judgment;
  • who made it and in what role;
  • date;
  • domain-to-final-rating rationale;
  • dissent or unresolved issues;
  • source IDs.

The label describes confidence in an estimate for an outcome in a defined context. It is not a recommendation and does not imply safety, effectiveness, or authorization.

Evidence to Decision

Recommendation development is outside the automated helper. A qualified panel using an applicable GRADE Evidence-to-Decision framework must explicitly consider, as relevant:

  • priority of the problem;
  • desirable and undesirable effects;
  • certainty of evidence;
  • values and variability;
  • resources and cost effectiveness;
  • equity;
  • acceptability;
  • feasibility.

Keep the evidence profile and any later recommendation record separate and traceable.

Quality-Control Checklist

  • Search and selection methods are documented.
  • Outcome definitions and horizons match the question.
  • All important benefits and harms are represented.
  • Effect estimates include uncertainty.
  • Each domain has a human judgment and rationale.
  • Every rationale links to source IDs.
  • The final certainty is outcome-specific.
  • Conflicts of interest and panel roles are recorded.
  • Disagreements and updates are versioned.
  • No patient-specific or treatment directive appears.

Helper

python3 scripts/evidence_profile_check.py assets/evidence_profile_template.json

The distributed template intentionally contains unresolved judgments. A non-zero result is expected until qualified humans complete it.

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