Multimodal models in oncology can produce accurate predictions, but accurate prediction does not reveal whether the model has learned biology that is shared across modalities, biology confined to one modality, or spurious correlations that reflect confounders rather than genuine biology. We introduce DECAT, a model-agnostic post-hoc evaluation framework that classifies multimodal representations into four diagnostic scenarios (shared, spurious, modality-specific, or no signal) for a given task and modality, using five null-referenced metrics and a rule-based decision procedure. The framework operates on learned representations, requires no knowledge of which specific confounder is present, and returns indeterminate when the evidence is insufficient. We evaluate multimodal architecture properties using DECAT on synthetic data across four multimodal model classes spanning over 2,500 trained representations, and demonstrate its operational use on 8,979 TCGA patients with both multimodal model embeddings and five pretrained pathology foundation models. Entangled models (e.g., CLIP) detect shared signal near-perfectly on synthetic data but also falsely claim it when it is absent, doing so in the majority of such cases on real foundation model embeddings. This false claim rate increases with signal strength and sample size, so that larger cohorts and stronger representations produce more confident but still incorrect diagnoses. Applied to both multimodal TCGA embeddings and five pathology foundation models without paired RNA, DECAT detects confounding invisible to AUROC without requiring the confounder labels, as confirmed by post-hoc stratification.

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