Improvements in Therapy Experience With Evoked Compound Action Potential Controlled, Closed-Loop Spinal Cord Stimulation—Primary Outcome of the ECHO-MAC Randomized Clinical Trial
Bibliographic record
Abstract
Spinal cord stimulation (SCS) is a well-established treatment for chronic neuropathic pain. However, over- or underdelivery of the SCS may occur because the spacing between the stimulating electrodes and the spinal cord is not fixed; spacing changes with motion and postural shifts may result in variable delivery of the SCS dose and, in turn, a suboptimal therapy experience for the patient. The evoked compound action potential (ECAP)-a measure of neural activation-may be used as a control signal to adapt SCS parameters in real time to compensate for this variability. In this prospective, multicenter, randomized, single-blind, crossover trial, reduction in overstimulation intensity was used as a perceptual measure to evaluate a novel ECAP-controlled, closed-loop (CL) SCS algorithm relative to traditional open-loop (OL) SCS. The primary outcome used a Likert scale to assess sensation during activities of daily living with CL versus OL SCS. Of the 42 subjects in the intent-to-treat analysis set, 97.6% had a reduction in sensation with CL versus OL SCS. The primary objective was met as the lower confidence limit (87.4%) exceeded the performance goal of 50% (P < .001). A total of 88.1% (37/42) of subjects preferred CL and 11.9% (5/42) preferred OL SCS. SCS dose consistency during CL SCS was demonstrated by the reduced variability in ECAP amplitude with CL SCS (standard deviation: 8.72 µV) relative to OL SCS (standard deviation: 19.95 µV). Together, these results demonstrate that the ECAP-controlled, CL algorithm reduces or eliminates unwanted sensation, and thereby provides a more preferred and consistent SCS experience. PERSPECTIVE: Patients with chronic pain need durable and dependable options for pain relief. SCS is an important therapy option, and new technology advancements could improve long-term therapy use. CL SCS offers a preferred and more consistent therapy experience for patients that could lead to increased therapy utilization and reliable therapy outcomes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".