Reliable clinical prediction requires both accuracy and principled uncertainty quantification, so that a model can flag cases where it lacks a coherent basis for its prediction and defer to a clinician. We introduce the Temporal Epistemic Graph of Thought (TEGoT), a framework that fuses free-text clinical notes with structured temporal events and models uncertainty over latent reasoning structure. Patient trajectories are represented as temporal graphs integrated with note embeddings, and a variational reasoning graph induces a distribution over latent topologies, decomposing predictive uncertainty into aleatoric, epistemic, and structural components, where structural uncertainty measures variability across inferred reasoning graphs. On a retrospective MIMIC-IV ICU mortality task, using structured events available before 12 hours and discharge-summary-derived note embeddings as a proof-of-concept text source, TEGoT reaches AUROC 0.931, AUPRC 0.655, and ECE 0.021 after temperature scaling, comparable to or above tabular, neural, and graph baselines under the same cohort. Structural uncertainty gives stronger error detection (AUROC 0.724) than standard epistemic uncertainty (0.681), and its weak correlation with epistemic uncertainty (𝜌=0.19) indicates a complementary signal rather than a repackaging of dropout variance. Using this signal for selective prediction raises retained AUROC from 0.931 to 0.963 at 50% coverage, with a net benefit of +0.083 over treat-all and a decision-curve-positive range of [0.05, 0.42], supporting reliable abstention. We argue that the social value of a clinical model should be measured not by discrimination alone but by the harm it averts when it defers, and we show that net benefit and risk–coverage trade-offs offer concrete, deployment-relevant ways to quantify that benefit.