ArticleJournal of clinical medicine2021
Machine Learning Algorithms Provide Greater Prediction of Response to SCS Than Lead Screening Trial: A Predictive AI-Based Multicenter Study.
Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 24 citations in OpenAlex.
- Artificial intelligence for prediction of clinical response and therapeutic value in interventional pain management: a scoping review.Frontiers in digital health · 2026Pooled it
- Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025Pooled it
- Study on Automatic Multi-Classification of Spine Based on Deep Learning and Postoperative Infection Screening.Journal of healthcare engineering · 2022Trial
- Emerging technologies in interventional pain management: a scoping review on current innovations and future directions.BMC anesthesiology · 2026Article
- From Prediction to Monitoring: Toward a Translational Framework of Biomarkers in Spinal Cord Stimulation.Biomedicines · 2026Review
- Artificial intelligence in spine care: A scoping review of treatment applications.North American Spine Society journal · 2026Review
- Utilizing Artificial Intelligence for the Diagnosis, Assessment, and Management of Chronic Pain.Journal of biomedical physics & engineering · 2025Review
- Resting-state brain functional connectivity in patients with chronic intractable pain who respond to spinal cord stimulation therapy.British journal of anaesthesia · 2025Article
- A digital tool for multidimensional assessment and prediction of treatment effectiveness in chronic pain management.iScience · 2024Article
- The Role and Applications of Artificial Intelligence in the Treatment of Chronic Pain.Current pain and headache reports · 2024Review
- Machine Learning in Pain Neuromodulation.Advances in experimental medicine and biology · 2024Review
- Real-world outcomes of single-stage spinal cord stimulation in chronic pain patients: A multicentre, European case series.Interventional pain medicine · 2023Article
- Machine Learning in Spinal Cord Stimulation for Chronic Pain.Operative neurosurgery (Hagerstown, Md.) · 2023Article
- Research hotspots and trends on spinal cord stimulation for pain treatment: a two-decade bibliometric analysis.Frontiers in neuroscience · 2023Article
- Combining Awake Anesthesia with Minimal Invasive Surgery Optimizes Intraoperative Surgical Spinal Cord Stimulation Lead Placement.Journal of clinical medicine · 2022Article
- Patient Selection for Spinal Cord Stimulation in Treatment of Pain: Sequential Decision-Making Model - A Narrative Review.Journal of pain research · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Persistent pain after spinal surgery can be successfully addressed by spinal cord stimulation (SCS). International guidelines strongly recommend that a lead trial be performed before any permanent implantation. Recent clinical data highlight some major limitations of this approach. First, it appears that patient outco mes, with or without lead trial, are similar. In contrast, during trialing, infection rate drops drastically within time and can compromise the therapy. Using composite pain assessment experience and previous research, we hypothesized that machine learning models could be robust screening tools and reliable predictors of long-term SCS efficacy. We developed several algorithms including logistic regression, regularized logistic regression (RLR), naive Bayes classifier, artificial neural networks, random forest and gradient-boosted trees to test this hypothesis and to perform internal and external validations, the objective being to confront model predictions with lead trial results using a 1-year composite outcome from 103 patients. While almost all models have demonstrated superiority on lead trialing, the RLR model appears to represent the best compromise between complexity and interpretability in the prediction of SCS efficacy. These results underscore the need to use AI-based predictive medicine, as a synergistic mathematical approach, aimed at helping implanters to optimize their clinical choices on daily practice.
Indexed as
Identifiers
What OpenQuestion holds
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.