Pain intensity and opioid consumption after temporary and permanent peripheral nerve stimulation: a 2-year multicenter analysis
Bibliographic record
Abstract
OBJECTIVE: Peripheral nerve stimulation (PNS) is an emerging neuromodulation modality, yet there remains limited data highlighting its long-term effectiveness. The objective of this study was to report real-world data on pain intensity and opioid consumption after temporary and permanent PNS for chronic pain up to 24 months postimplantation. METHODS: A retrospective study was conducted on all patients who received PNS implants at a multi-centered enterprise between January 1, 2014 and February 24, 2022. The two co-primary outcomes were: (1) change in pain intensity (11-point Numerical Rating Scale) from baseline to 12 months postimplant; and (2) comparison of the change in pain intensity between temporary and permanent PNS cohorts 12 months postimplant. RESULTS: 126 patients were included in this analysis. Pain intensity significantly decreased 12 months postimplant in the overall cohort (mean difference (MD) -3.0 (95% CI -3.5 to -2.4), p<0.0001). No significant difference in this reduction was identified between temporary and permanent PNS cohorts (MD 0.0 (95% CI -1.1 to 1.0), p=1.00) 12 months postimplantation. Pain intensity significantly decreased in the overall, temporary, and permanent cohorts at all secondary time points (3, 6, and 24 months). No change in daily opioid consumption was observed at 6 and 12 months postimplant in the overall cohort. CONCLUSION: This study found that both temporary and permanent PNS may be effective for reducing pain intensity in patients with chronic pain up to 24 months postimplantation, although no changes in opioid consumption were observed. The decrease in pain intensity was comparable between patients receiving temporary versus permanent implants, highlighting that temporary PNS may achieve long-lasting clinical benefits. However, given the substantial loss to follow-up, further large-scale studies are needed to solidify conclusions about the efficacy of 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".