ArticleJOR spine2025
Exploring the Biopsychosocial Impact of Chronic Low Back Pain in Workers Through Artificial Intelligence-Driven Phenotyping.
Article in JOR spine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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
4 citing papers in PubMed.
- Article
- Platelet-rich plasma and stem cell therapies for spondylosis: a systematic review of randomized controlled trials.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Review
- Article
- Exploring the Biopsychosocial Impact of Chronic Low Back Pain in Workers Through Artificial Intelligence-Driven Phenotyping.JOR spine · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Study Design: Cross-sectional retrospective cohort study. Objectives: Chronic low back pain (cLBP) is a major cause of disability worldwide, significantly affecting return to work (RTW). This study aimed to assess the biopsychosocial factors influencing occupational functioning in workers with cLBP using artificial intelligence (AI) data-driven patient phenotyping. Methods: Data of workers affected by cLBP were collected through a comprehensive assessment of demographic, clinical, and occupational factors. Hierarchical clustering was employed to identify distinct phenotypes based on patient-reported outcome measures (PROMs), including the Oswestry Disability Index (ODI), Visual Analog Scale (VAS), Work Ability Index (WAI), Nordic score, and Patient Health Questionnaire-2 (PHQ-2). Independent Results: A total of 304 patients were included in the analysis. The AI-driven phenotyping approach identified two distinct clusters, representing 51% (Cluster 1) and 49% (Cluster 2) of the dataset. Compared to Cluster 1, Cluster 2 exhibited significantly higher absenteeism (17.00 vs. 5.22 days, Conclusion: Our study identified the most relevant PROMs differentiating between cLBP clusters of patients, emphasizing different levels of absenteeism and pain-related outcomes.These findings contributed to unravel the data-driven AI potential in suggesting personalized interventions targeting specific biopsychosocial profiles, which may improve clinical outcomes and occupational functioning in workers with cLBP, ultimately enhancing their overall well-being.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.