Patient-reported pain location is diagnostically decisive for some presentations and nearly uninformative for others. The prevailing account treats this as a single gradient of diagnostic utility governed by anatomical complexity. That explanation conflates three epistemically distinct failures of localization, each with its own mathematical structure, optimal instrument, and public-health consequence. In anatomical multiplexing (a), many structures share one location: a non-identifiable inverse problem. In delocalized amplification (b) --- clinically, central sensitization or nociplastic pain --- a centrally driven pain-behaviour pattern replaces the peripheral generator: a change of generative model. In referred/atypical displacement (c), location shifts in a systematic, person-dependent way, as in referred pain and in atypical presentations: a covariate-dependent bias. The three behave differently under inverse-problem, information-theoretic, decision-theoretic, and neural-field models, and demand different remedies. A single principle unifies them: they are one Bayesian inference problem failing at different nodes, meaning different points in one generative model: the likelihood, the model class, and the prior and loss. Observation over time increases the recoverable information. A fourth node, the report itself, carries this paper's main formal contribution --- a spatial Bayesian model of where pain is said to be, distinct from where it is felt. Re-examination further finds that the published "high-utility" accuracy band leans on overstated specificity, so the gradient is real but flatter than drawn. The account sits on a "why-location-fails" axis, a synthesis distinct from the nociceptive/neuropathic/nociplastic taxonomy (Kosek et al., 2016).

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