ArticleSpine2026
An Unsupervised Learning Approach for Multimodal Low Back Pain Stratification.
Article in Spine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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Who cites it
1 citing paper in PubMed.
- Deviations from recommended use of liposomal bupivacaine: a real-world pharmacovigilance study using the FAERS database.Frontiers in medicine · 2026Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
designCross-sectional study.
objectiveThis study proposes a novel stratification framework for individuals with low back pain (LBP). The method integrates Northern Finland Birth Cohort data comprising imaging biomarkers from deep learning (DL)-based analysis of lumbar spine MRI with the data on smoking status, demographics (sex and BMI), self-reported data from Örebro Musculoskeletal Pain Screening Questionnaire (ÖMPSQ) short and the STarT Back Tool (SBT). Furthermore, the utility of this stratified approach was validated by demonstrating a superior net benefit compared with "treat-all" strategy.
backgroundCurrent risk stratification for individuals with LBP relies on ÖMPSQ short and SBT among others. While these tools are invaluable for capturing psychosocial characteristics predictive of future disability and functional outcomes, LBP's multifactorial nature necessitates a more comprehensive framework for effective risk stratification. MATERIALS AND
methodsA method for multimodal unsupervised patient stratification has been developed that allows for the integration of imaging biomarkers of disc degeneration (DD) and facet tropism (FT), extracted using DL models, with nonimaging data. The framework utilized robust K-Means clustering to stratify individuals. Clusters were characterized using LBP frequency and bothersomeness, and their robustness was validated with a multi-class logistic regression model. Net benefit was assessed through decision curve analysis.
resultsThree distinct subgroups were characterized by LBP frequency and bothersomeness. One subgroup was dominated by psychosocial characteristics (psychosocial risk P < 0.05), the second by physical degenerative changes (DD P < 0.05), and the third by a mix of both. Predictive models for cluster assignment were robust, achieving high mean accuracies (SBT-based: 0.89; ÖMPSQ-short-based: 0.87). The net benefit is superior throughout a range of threshold probabilities compared with a "treat-all" strategy.
conclusionA novel framework was developed that integrates multimodal data to identify distinct subgroups differentiated by their physical and psychosocial characteristics in a population-based cohort, demonstrating potential for advancing personalized care.
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