Impact of adjuvant analgesics on outcomes of spinal cord and peripheral nerve stimulation: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Spinal cord stimulation (SCS) and peripheral nerve stimulation (PNS) are used to treat refractory pain, but even well-selected patients can fail to have analgesic benefit following implantation. The analgesia afforded by SCS/PNS may be enhanced or attenuated by the ongoing use of analgesic medications that are often consumed by patients who receive SCS and PNS implants. We undertook a scoping review to scan and summarize the evidence for impact of adjuvant pharmacotherapy on SCS and PNS therapy in animal and human settings. MATERIALS AND METHODS: A comprehensive medical literature review was performed on major medical databases including MEDLINE, EMBASE, CINAHL, CENTRAL, and Google Scholar from inception until July 31, 2024, for both human and animal studies. Data on the effect of pharmacotherapy on SCS analgesic efficacy and adverse effects were extracted and summarized using the Arksey and O'Malley population, concept, context model for scoping reviews. RESULTS: Twenty-seven studies, 9 on animals and 18 on humans were identified. In human studies, SCS non-responders with neuropathic pain had analgesia restored by addition of intrathecal baclofen and clonidine. Patients who eliminated opioid use, or who were opioid naive, had superior clinical outcomes with SCS compared to those continuing opioids. Cannabinoids were associated with enhanced SCS analgesia. Patients on benzodiazepines had higher likelihood of SCS explantation. Animal studies showed intrathecal ketamine restored SCS analgesic benefits, while baclofen, clonidine, cannabinoid receptor agonists, tricyclic antidepressants, serotonin, and norepinephrine reuptake inhibitors, augmented SCS responses while benzodiazepines were found to inhibit analgesic effects of PNS. CONCLUSIONS: This review indicates that adjunctive analgesic therapy may play a significant role in either enhancing or attenuating analgesic benefits from SCS and PNS. By optimizing the use of analgesic medications, it may be possible to restore or enhance pain relief from both SCS and PNS.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".