aceDetermination of candidate predictors for chiropractic treatment outcome of spinal pain using a machine learning framework for small datasets
Objectives To develop a machine learning (ML) approach to explore self-reported factors predictive for recovery in a small set of spinal pain patients. Methods In this prospective cohort study, patients ( N = 96; mean age = 44.5 ± 16.5 years; 53 female) completed an extensive questionnaire at baseline and after 1 and 3 months. Prediction targets were defined as binary outcomes (recovery/non-recovery) based on improvement at 3 months for pain intensity, disability, quality of life, and Patient Global Impression o…
A three-step ML framework using SHAP and cross-validation achieved high LOOCV AUCs and flagged self-reported factors like treatment expectations and self-efficacy as candidate predictors of recovery at 3 months in spinal pain patients receiving chiropractic care.
Predictive performance dropped from LOOCV to ensemble CV and the small N=96 phenotypically rich dataset means results remain hypothesis-generating without prospective replication in adequately powered cohorts.
Findings are exploratory and limited by small sample size and generalizability concerns, with a notable performance drop between validation schemes requiring replication in larger cohorts.
Evidence
- Peer-reviewedPain Management2026-09-15
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Truvace Impact Record TRV-2026-1109, v1: “Determination of candidate predictors for chiropractic treatment outcome of spinal pain using a machine learning framework for small datasets.” Truvace, 2026-09-16. /record/TRV-2026-1109 (accessed at citation time). sha256 7a9b86a54542b3ea…
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