Guideline‐Anchored Interval Metric: A Boundary‐Anchored Transformation for Heterogeneous Quantitative Measurements

Background Quantitative biometric measurements are expressed in heterogeneous units and numerical ranges, complicating their representation on a unified interpretive scale. Conventional standardization provides cross‐variable comparability through population‐ or distribution‐derived parameters but remains distribution‐relative. A methodological gap arises when the intended reference information instead consists of externally specified paired boundaries. Objective To introduce the Guideline‐Anchored Interval Metric (GAIM), a stand‐alone, boundary‐anchored, piecewise‐linear framework for representing heterogeneous quantitative measurements relative to externally specified paired boundaries. Methodology Given positive lower (L) and upper (U) boundaries, GAIM defines a geometric reference center C = √(U × L) and direction‐specific Threshold‐Center Distance units, TCD l = C − L and TCDᵤ = U − C. Four interpretive zones map observations within and beyond the boundaries onto a continuous scale. By construction, L, C, and U map to GAIM values of −1.00, 0.00, and +1.00, respectively. Results Analytical verification and illustrative biomarker applications demonstrate that GAIM is continuous, strictly monotonic, direction‐sensitive, and boundary‐anchored. The four interpretive zones reduce mathematically to two linear branches joined at C. The geometric center provides multiplicative balance between the positive boundaries, while separate TCD units accommodate unequal lower and upper distances. GAIM is invariant under consistent positive multiplicative unit conversion. Once L and U are specified, transformation parameters are derived algebraically without contemporaneous estimation of cohort‐derived location or dispersion parameters. Conclusion GAIM provides a stand‐alone transformation for expressing heterogeneous quantitative measurements on a common boundary‐relative scale when valid positive paired boundaries are available. It offers an alternative reference architecture to distribution‐relative standardization when boundary‐relative interpretation is intended. Its validity depends on appropriate boundary selection, and its clinical utility requires empirical evaluation.