ArticleOperative neurosurgery (Hagerstown, Md.)2023
Machine Learning in Spinal Cord Stimulation for Chronic Pain.
Article in Operative neurosurgery (Hagerstown, Md.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed, 9 citations in OpenAlex.
- State of the Art in Neuromodulation or Spinal Cord Stimulation Therapy.Journal of clinical medicine · 2026Review
- Spinal cord stimulation: An emerging strategy for chronic pain relief after spinal cord injury.Neural regeneration research · 2026Article
- The Spinal Cord Stimulation Trial Success Score (STSS): A Narrative Review and Evidence-Informed Conceptual Framework for Structured Candidate Assessment.Journal of clinical medicine · 2026Review
- Leveraging artificial intelligence to optimize neuromodulation in the treatment of patients with chronic pain.Frontiers in pain research (Lausanne, Switzerland) · 2026Review
- Ethics of AI in healthcare: a scoping review demonstrating applicability of a foundational framework.Frontiers in digital health · 2025Review
- The Role and Applications of Artificial Intelligence in the Treatment of Chronic Pain.Current pain and headache reports · 2024Review
- Artificial intelligence and pain management: cautiously optimistic.Pain management · 2024Article
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 at 2 institutions in 1 country.
Funding
Abstract
Spinal cord stimulation (SCS) is an effective treatment for chronic neuropathic pain. The success of SCS is dependent on candidate selection, response to trialing, and programming optimization. Owing to the subjective nature of these variables, machine learning (ML) offers a powerful tool to augment these processes. Here we explore what work has been done using data analytics and applications of ML in SCS. In addition, we discuss aspects of SCS which have narrowly been influenced by ML and propose the need for further exploration. ML has demonstrated a potential to complement SCS to an extent ranging from assistance with candidate selection to replacing invasive and costly aspects of the surgery. The clinical application of ML in SCS shows promise for improving patient outcomes, reducing costs of treatment, limiting invasiveness, and resulting in a better quality of life for the patient.
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.