Finding Optimal Neuromodulation for Chronic Pain: Waves, Bursts, and Beyond
Bibliographic record
Abstract
BACKGROUND: Spinal cord stimulation (SCS) has emerged as state-of-the-art evidence-based treatment for chronic intractable pain related to spinal and peripheral nerve disorders. Traditionally delivered as steady-state, paraesthesia-producing electrical stimulation, newer technology has augmented the SCS option and outcome in the last decade. OBJECTIVE: To present an overview of the traditional and newer SCS waveforms. MATERIALS AND METHODS: We present a short literature review of SCS waveforms in reference to newer waveforms and describing paraesthesia-free, high frequency, and burst stimulation methods as well as advances in waveform paradigms and programming modalities. Pertinent literature was reviewed, especially in the context of evolution in the waveforms of SCS and stimulation parameters. RESULTS: Conventional tonic SCS remains one of the most utilized and clinically validated SCS waveforms. Newer waveforms such as burst stimulation, high-frequency stimulation, and the sub-perception SCS have emerged in the last decades with favorable results with no or minimal paraesthesia, including in cases otherwise intractable to conventional tonic SCS. The recent evolution and experience of closed-loop SCS is promising and appealing. The experience and validation of the newer SCS waveforms, however, remain limited but optimistic. CONCLUSIONS: Advances in SCS device technology and waveforms have improved patient outcomes, leading to its increased utilization of SCS for chronic pain. These improvements and the development of closed-loop SCS have been increasingly promising development and foster a clinical translation of improved pain relief as the years of research and clinical study beyond conventional SCS waveform come to fruition.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".