ArticleScientific reports2025
Advanced machine learning applications in fibromyalgia to assess the relationship between 3D spinal alignment with clinical outcomes.
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 2 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
2 citing papers in PubMed.
- Three-Dimensional Surface Topography for the Assessment of Spinal Alignment: A Cross-Sectional Study of Biomechanical Correlates.Diagnostics (Basel, Switzerland) · 2026Article
- Moving Toward Objective Diagnosis in Fibromyalgia: Emerging Biomarkers and Digital Phenotyping Tools.Biomedicines · 2026Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
This study leveraged machine learning (ML) models to explore the relationship between three-dimensional (3D) spinal alignment parameters and clinical outcomes in patients suffering from fibromyalgia syndrome (FMS). A cohort of 303 FMS patients, diagnosed according to the 2016 American College of Rheumatology criteria, underwent comprehensive assessments of sagittal imbalance, coronal imbalance, vertebral rotation, pelvic obliquity, pelvic torsion, and pelvic rotation using a validated 3D imaging system. Clinical outcomes, included the fibromyalgia impact questionnaire (FIQ), pain catastrophizing scale (PCS), Pittsburgh sleep quality index (PSQI), and algometric pain scores. Five ML models were employed: Fast Kolmogorov-Arnold Networks with Bee Colony Optimization (FastKAN-BCO), FastKAN with LBFGS, Multilayer Perceptron with LBFGS (MLP-LBFGS), Multilayer Perceptron with ADAM (MLP-ADAM), and linear regression. Among the models tested, FastKAN-BCO demonstrated the highest R-squared value (0.95) for algometric pain, while the MLP-LBFGS model achieved superior performance for PCS (R
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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 OpenQuestion graph.