Leave-one-out (LOO) cross-validation, a standard tool for Bayesian model assessment, evaluates each observation using a LOO predictive distribution derived from the model itself. In this work, we introduce an alternative construction in which posterior predictive simulation connects the Bayesian model to an external pretrained predictive learner. Our method, Predictive-Context LOO (PC-LOO), fits a Bayesian model once, generates posterior-predictive synthetic data, and uses them as context for the learner to approximate each LOO predictive density. Under an oracle construction, averaging contexts generated from the LOO posterior recovers the exact LOO predictive density. Practical PC-LOO replaces the oracle predictor by the pretrained learner and the LOO posterior by the full posterior. We quantify these errors through learner compatibility $C_i$ and deletion influence $V_i$, obtaining $H^2 (p^{LOO}_{i} , q^{PC}_{i}) \leq C_{i} + V_{i}$. The identity also holds for any fixed subset of $M$ context points, allowing $M \ll n - 1$ as the learner's context budget, and scalability to large datasets. Experiments with TabICL support this decomposition, deletion influence decays approximately as $n^{-1}$, while learner compatibility can remain limiting at large $n$. PC-LOO remains accurate with reduced context sizes, while model comparison accuracy is governed by differences in approximation error across models rather than their absolute errors. More broadly, PC-LOO establishes posterior predictive simulation as a principled interface for integrating pretrained predictive learners into Bayesian workflows.

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