Conventional meta-analysis summarizes evidence through pooled estimates, intervals, and p-values, but these outputs do not directly measure evidence for an effect, evidence for no effect, or the degree to which conclusions depend on publication selection or small-study effects. We introduce a corpus-scale Bayesian evidential-audit workflow for meta-analytic corpora. The workflow reconstructs or accepts study-level effects and standard errors, harmonizes directions, fits a matched Bayesian random-effects baseline and a bias-aware model-averaged ensemble, and reports paired estimates with component and joint model-family evidence. The central estimand is rigor: a joint Bayes-factor summary combining resolved effect/no-effect evidence with absence of an explicit bias component in the fitted ensemble. Rigor is not a positive-finding score; no-effect evidence can score highly, whereas inconclusive or bias-dependent evidence scores poorly. We characterize the workflow using an ADEMP-framed simulation/resampling design with known-cell synthetic simulation, empirical registry resampling, and empirical fitted-profile-weighted synthetic sampling. A nutrition intervention corpus provides the worked case study, where bias-aware fitting often attenuates conventional estimates and many nominally meaningful effects lose clean evidential support. A public companion repository provides empirical inputs, generated artifacts, simulation source/design files, and documentation for reproducing and adapting the audit.

翻译：传统的元分析通过合并估计值、置信区间和p值来总结证据，但这些输出并不能直接衡量支持效应存在的证据、支持无效应的证据，或者结论在多大程度上依赖于发表偏倚或小样本效应。我们提出了一种针对元分析语料库的语料库级别贝叶斯证据审计工作流。该工作流重构或接受研究层面的效应值和标准误，统一方向，拟合匹配的贝叶斯随机效应基线模型和偏差感知模型平均集成，并报告成对估计值以及分量和联合模型族证据。核心估计目标是严谨性：一种联合贝叶斯因子汇总，将已解决的效应/无效应证据与拟合集成中不存在显式偏差分量相结合。严谨性并非正向发现评分；无效应证据可以获得高分，而不确定或依赖偏差的证据得分较低。我们使用ADEMP框架下的模拟/重采样设计来表征工作流，包括已知单元合成模拟、经验注册库重采样以及经验拟合轮廓加权合成采样。一个营养干预语料库提供了工作案例研究，其中偏差感知拟合通常会削弱传统估计值，许多名义上有意义的效应也失去了清晰的证据支持。一个公开的配套代码仓库提供了经验输入、生成产物、模拟源代码/设计文件以及用于复现和调整审计的文档。