Role of peripheral nerve stimulation in treating chronic neuropathic pain: an international focused survey of pain medicine experts
Bibliographic record
Abstract
Interventional pain management (IPM) options for refractory neuropathic pain (NP) have recently increased with availability of peripheral nerve stimulation (PNS) equipment and expertise. Given a lack of high-quality evidence and guidelines on this topic, we sought to understand the perception of physicians with expertise in treating NP regarding IPM and the role of PNS. We emailed a survey in March 2022 to international NP experts including pain medicine physicians, researchers, and leaders of 11 professional pain societies. No representatives from vendors of PNS systems were included in the design of the survey nor as respondents. Among 24 respondents (67% of those contacted), the distal common peroneal, tibial, and sural nerves were most frequently targeted (60%) with PNS. Persistent postsurgical pain of more than 3 months was the most common indication for PNS (84%). The aggregate NP treatment algorithm in order of median rank was non-opioid medications as first line, IPM including epidural/perineural steroid injections tied with transcutaneous electrical nerve stimulation as second line, pulsed radiofrequency (RF) tied with RF ablation/denervation as third line, temporary then permanent PNS as fourth line, followed by spinal cord stimulation, opioids, cryoablation, botulinum, peripheral nerve field stimulation, intrathecal targeted drug delivery, and others. Before offering PNS, 12 respondents (50%) indicated their preference for trialing non-neuromodulation treatments for 1-3 months. Twenty-two respondents (92%) agreed PNS should be offered early in the treatment of neuropathic pain. The most common barriers to PNS use were cost, lack of high-quality evidence in support of its use, lack of exposure to PNS in training programs, and lack of familiarity with the use of ultrasound guidance. PNS appears to have an increasing role in the treatment of NP but more research is needed on the outcomes of PNS to elucidate its role.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".