ArticleScientific reports2025
Explainable AI reveals tissue pathology and psychosocial drivers of opioid prescription for non-specific chronic low back pain.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Explainable Artificial Intelligence in Healthcare: Current Landscape, Challenges, and Future Directions.Health science reports · 2026Review
- Manual therapy combined with core muscle training for chronic non-specific low back pain in sedentary individuals: a study protocol for a single-center randomized controlled trial.Frontiers in medicine · 2026Article
- Explainable Machine Learning Applied to Bioelectrical Impedance for Low Back Pain: Classification and Pain-Score Prediction.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Effective management of non-specific chronic lower back pain (ns-cLBP) requires nuanced prescription decisions within evolving guidelines for conservative treatment. This study developed comprehensive LBP patient profiles from electronic medical records (EMR), integrating clinical charts (demographics, social determinants, diagnoses, medications) and radiology reports (MRI-confirmed diagnoses) to predict pharmacological management strategies. One-vs-one and one-vs-rest classification frameworks systematically evaluated treatment decisions across three prescriptions: no medication, NSAIDs, and opioids. Real-world complexity and heterogeneity in ns-cLBP management was reflected in modest yet clinically meaningful performance metrics (balanced accuracy = 0.58, AUC = 0.62, F1-score = 0.42). Chart-documented diagnoses marginally outperformed MRI-reported pathology as predictors, though this difference was within the range of variability, which suggests the importance of diagnoses informed by patient-reported symptoms in shaping treatment pathways. SHAP feature importance analysis identified consistent predictors (year_at_first_imaging) and variable factors (spinal_stenosis, disc_pathology, race_ethnicity, negative_psych_state, osteoarthritis_osteoarthrosis) in prescriptions, with higher associations observed in those with anxiety or depression, partnered individuals and females. By leveraging explainable AI, this study quantifies the interplay between biological and psychosocial drivers of prescribing decisions, offering a transparent, data-driven monitoring tool for understanding in chronic pain care. These findings demonstrate the potential of multi-modal EMR data and interpretable models to guide more personalized, equitable ns-cLBP management and opioid prescriptions.
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Registered trials
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