Spectral clustering identifies patterns of chiropractic care in a national longitudinal cohort.

Researchers applied spectral clustering—a machine learning technique—to a national longitudinal dataset to identify distinct patterns of chiropractic care utilization among patients. The study demonstrates how advanced data-driven methods can reveal subgroups with different care trajectories, which may have implications for understanding musculoskeletal treatment pathways relevant to physical therapists. Findings like these could inform comparative effectiveness research across professions treating similar conditions.

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