Overlapping Sanders scores challenge age-based AIS/JIS classifications.

Weintraub, Matthew; Taha, Omar; Elfilali, Mehdi M; Barile, Joseph G; McQuerry, Jessica L; Guillaume, Tenner; Vitale, Michael G; Pediatric Spine Study Group (PSSG) et al. · Spine Deform · 2026

retrospective_cohort · Level III

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Abstract

The current classification of idiopathic scoliosis relies on chronological age cutoffs to differentiate juvenile (JIS) from adolescent (AIS) subtypes. However, age-based distinctions may not reliably reflect physiological maturity, a critical factor for predicting curve progression and guiding treatment. This study investigates skeletal maturity differences across the traditional JIS-AIS age threshold using the Sanders maturity scale (SMS). A retrospective review was conducted using a multicenter pediatric spine registry. Patients aged 7-13 years with idiopathic scoliosis and documented SMS scores were included. SMS distributions were analyzed across age bands surrounding the JIS-AIS cutoff (9-10 vs. 10-11 years). Demographic and anthropometric data evaluated factors associated with skeletal maturity variation. Among 637 patients (86% female and 14% male), there was a 50% overlap in SMS scores between 9-10 and 10-11-year-olds, with many AIS-classified patients exhibiting skeletal immaturity similar to JIS counterparts. SMS stages 3-5 were associated with greater height, weight, and BMI than stages 1-2 within the same age range. Female and non-Caucasian patients were more likely to show advanced skeletal maturity. These findings underscore significant heterogeneity in growth potential near the age-based diagnostic boundary. Chronological age alone does not reliably reflect skeletal maturity or growth risk in idiopathic scoliosis patients. The Sanders Maturity Scale offers a more precise, physiology-based alternative to age-based classification and should be considered in diagnostic, prognostic, and treatment frameworks. Transitioning toward skeletal maturity-based classification could enhance treatment individualization, improve clinical trial stratification, and optimize patient outcomes.