Trends in Clinical Trials for Spinal Cord Stimulation
Bibliographic record
Abstract
BACKGROUND: Spinal cord stimulation (SCS) is a neuromodulation technology widely used in the treatment of intractable chronic pain syndromes. SCS is now being applied more broadly as a possible therapy for a range of indications, including neurological, cardiac, and gastrointestinal disorders. Ongoing research in this field is critical in order to gain further insights into the mechanisms of SCS, determine its role in new indications, and refine programming techniques for the optimization of therapeutic outcomes. OBJECTIVE: To assess the state of SCS-related human research by cataloging and summarizing clinical trials that have been recently completed or are currently underway in this field. METHODS: A search was conducted for clinical trials pertaining to SCS using the ClinicalTrials.gov database. Trials were analyzed to generate a detailed overview of ongoing SCS-related research. Specifically, trials were categorized by intervention, trial start date, study completion status, clinical phase, projected subject enrollment, condition, country of origin, device manufacturer, funding source, and study topic. RESULTS: In total, 212 relevant clinical trials were identified. 175 trials (82.5%) involved invasive SCS, while the remaining 37 trials (17.5%) used noninvasive forms of spinal stimulation. Most trials examined the efficacy of SCS for chronic pain syndromes or new indications, while others assessed different stimulation parameters. The studies spanned >27 different disorders, with almost 20% of trials pertaining to conditions other than chronic pain syndromes. The majority of SCS trials were US-based (55.7% of studies), but many countries (e.g., Belgium and UK) are becoming increasingly active. The ratio of investigator-sponsored to industry-sponsored trials was 2:1. Emphasizing the need to optimize therapeutic outcomes of SCS, one-quarter of trials predominantly focused on the assessment of alternative stimulation parameters such as burst or high-frequency stimulation. CONCLUSIONS: A large number of clinical trials of SCS are underway. Improvements in the treatment of pain and novel indications for SCS constitute the majority of studies. This overview of SCS-related clinical trials provides a window into future new indications, novel stimulation techniques, and a heightened understanding of the mechanisms of action.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.196 | 0.395 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.036 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".