Predictability of Postural and Standing Balance from a Clinical Impairment Battery in Children with Cerebral Palsy: A Cross-Validated Regression and Hurdle Model Analysis

(1) Background: Balance determines functional status in cerebral palsy (CP), yet its predictability from clinical impairment measures remains unclear. This study aims to evaluate the predictability of three balance targets using a clinical impairment battery and determine whether standing balance comprises discrete and graded components. (2) Methods: A total of 173 children with CP (aged 0.9–9.0 years) were assessed using the Early Clinical Assessment of Balance (ECAB) (ECAB total, Part 1: head/trunk control, Part 2: standing). Spasticity, muscle strength, posture, tone asymmetry, age, sex, and CP subtype were analyzed as predictors using cross-validated regression models and a two-part hurdle model. (3) Results: Head and trunk control demonstrated high predictability (R2 = 0.807), while the ECAB total score achieved R2 = 0.671. Standing balance showed moderate predictability in standard regression (R2 = 0.519). The hurdle model decomposed this: it discriminated standing capability with high accuracy (AUC = 0.982) but explained only 41.4% of graded variance among standers (R2 = 0.414), clarifying rather than improving overall prediction. Muscle strength was the primary predictor across all models. (4) Conclusions: Within this cohort, clinical impairments reconstruct head and trunk control and predict whether a child can stand but do not explain standing quality; external validation is still required. The ECAB standing subscale captures graded information not reconstructable from the impairment battery as specified (which by design excludes global severity classification) and should be measured directly.