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TRUVACE RECORD VERSION
record: TRV-2026-1109
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-16T06:56:05.104654Z
status: published
lens: trace
sector: health
headline: Determination of candidate predictors for chiropractic treatment outcome of spinal pain using a machine learning framework for small datasets
dek: 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…
gain_title: 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.
problem_title: 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.
trace_subject: predicting 3-month recovery after chiropractic treatment for spinal pain using a small-sample machine learning framework
gain_reading: 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.
gain_evidence: Area under the curve (AUC) values were 0.93-0.99 in LOOCV and 0.62-0.90 in ensemble cross-validation (CV). | SHAP analyses revealed higher recovery odds with positive treatment expectations and higher self-efficacy, younger age, lower body mass index, and fewer comorbidities.
problem_reading: 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.
problem_evidence: Given the performance drop between LOOCV and ensemble CV, current findings are hypothesis-generating. | Prospective replication in adequately powered cohorts is necessary.
quick_read: Researchers developed a three-step machine learning approach to explore self-reported predictors of recovery in 96 spinal pain patients undergoing chiropractic care, using baseline questionnaires to predict binary recovery at 3 months across pain, disability, quality of life and global impression measures.

The work matters because it demonstrates a method to extract candidate prognostic factors from small but phenotypically rich clinical datasets, yet the drop from LOOCV AUCs of 0.93-0.99 to ensemble CV AUCs of 0.62-0.90 and the authors' own caution that findings are hypothesis-generating leave uncertainty about generalizability until larger prospective replication.
limitation: 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.
tag: Dual reading
key_points: Prospective cohort of N=96 spinal pain patients (mean age 44.5 ± 16.5 years; 53 female) completed extensive questionnaire at baseline and after 1 and 3 months. | Binary recovery targets were defined based on improvement at 3 months for pain intensity, disability, quality of life, and Patient Global Impression of Change. | ML pipeline included SHAP for candidate baseline feature selection, LOOCV and permutation testing for validation, and testing for ability to generalize.
rundown: The study enrolled 96 patients who completed questionnaires at baseline, 1 month and 3 months, with outcomes binarized as recovery/non-recovery for pain intensity, disability, quality of life, and Patient Global Impression of Change.

The authors used SHapley Additive exPlanations for feature selection, Leave-One-Out Cross-Validation and permutation testing for validation, and ensemble cross-validation to test generalization, reporting AUCs of 0.93-0.99 in LOOCV versus 0.62-0.90 in ensemble CV.

SHAP results linked higher recovery odds to positive treatment expectations, higher self-efficacy, younger age, lower BMI and fewer comorbidities, while psychological dysfunction generally hindered recovery.
sources:
- peer_reviewed | Pain Management | https://doi.org/10.1080/17581869.2026.2718881 | 2026-09-15
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