ArticleScientific reports2024
Utilizing machine learning to predict post-treatment outcomes in chronic non-specific neck pain patients undergoing cervical extension traction.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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
10 citing papers in PubMed.
- The Reliability of Biomechanical Mensuration Methods of the Sagittal Cervical Spine in Radiography Used in Clinical Practice: A Systematic Review of the Literature.Journal of clinical medicine · 2026Review
- Article
- Machine learning models for predicting treatment outcomes in chronic non-specific back pain patients undergoing lumbar extension traction.Scientific reports · 2026Article
- Association of brain cortical changes with efficacy of treatment in patients with chronic neck and shoulder pain: a longitudinal surface-based morphometry study.Frontiers in neuroscience · 2026Article
- Predicting acupuncture efficacy for neck pain based on functional connectivity features: a machine learning study.Annals of medicine · 2025Article
- The magnitude of sagittal head posture displacement and patient demographics predict rehabilitation outcomes in patients with chronic nonspecific neck pain.Scientific reports · 2025Article
- A comparative study on TB incidence and HIVTB coinfection using machine learning models on WHO global TB dataset.Scientific reports · 2025Article
- Scoping Review of Machine Learning and Patient-Reported Outcomes in Spine Surgery.Bioengineering (Basel, Switzerland) · 2025Review
- The Rationale and Safety of Routine Imaging in Rehabilitative Spine Care: Delayed Radiographs for Patients Presenting With Spine Disorders is Debatable.Dose-response : a publication of International Hormesis SocietyArticle
- Intra-individual structural covariance network in patients with chronic neck and shoulder pain: a longitudinal brain structure analysis.Frontiers in neurologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study explored the application of machine learning in predicting post-treatment outcomes for chronic neck pain patients undergoing a multimodal program featuring cervical extension traction (CET). Pre-treatment demographic and clinical variables were used to develop predictive models capable of anticipating modifications in cervical lordotic angle (CLA), pain and disability of 570 patients treated between 2014 and 2020. Linear regression models used pre-treatment variables of age, body mass index, CLA, anterior head translation, disability index, pain score, treatment frequency, duration and compliance. These models used the sci-kit-learn machine learning library within Python for implementing linear regression algorithms. The linear regression models demonstrated high precision and accuracy, and effectively explained 30-55% of the variability in post-treatment outcomes, the highest for the CLA. This pioneering study integrates machine learning into spinal rehabilitation. The developed models offer valuable information to customize interventions, set realistic expectations, and optimize treatment strategies based on individual patient characteristics as treated conservatively with rehabilitation programs using CET as part of multimodal care.
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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.