Electrical stimulation of the posterior tibial nerve reduces neuropathic pain in patients with polyneuropathy
Bibliographic record
Abstract
Abstract: Peripheral neuropathic pain (PNP) is caused by neuronal damage to the peripheral nervous system and usually affects the distal extremities. This open-label study examined the effect of short-term peripheral nerve stimulation (PNS) on individuals with PNP due to polyneuropathy. A total of 12 patients (mean age, 63.0 ± 10.0 years, 41.7% male) with daily bilateral PNP for at least 6 months (mean duration, 7.4 ± 7.8 years) received a total of six direct electrical stimulation therapies to the posterior tibial nerve at 3–4-day intervals. Eight patients completed the study and were included in the efficacy analysis. The average pain at baseline was 36.6 ± 3.80 estimated by the Short-Form McGill Pain Questionnaire. After the last stimulation, pain was significantly reduced by 85.5% to 4.88 ± 3.1 ( p = 0.008). Six patients (75%) had over 50% decrease in pain after the first stimulation therapy and 99.2% after the final stimulation therapy. The patients also reported statistically significant decreases in pain level (measured by visual analog scale), ranging from 54.85% to 87.50% after each of the stimulations as compared to the pain experienced prior to the stimulations. The procedure was safe without any serious adverse events. PNS has demonstrated excellent efficacy and improvement of PNP symptoms. Further studies in larger patient populations are warranted. Keywords: peripheral nerve stimulation, posterior tibial nerve stimulation, peripheral neuropathic pain, quality of life
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".