Tibial nerve stimulation with a miniature, wireless stimulator in chronic peripheral neuropathic pain
Bibliographic record
Abstract
Abstract: Peripheral neuropathic pain (PNP) and complex regional pain syndrome (CRPS) can be effectively treated with peripheral nerve stimulation. In this clinical trial report, effectiveness of novel, miniature, wirelessly controlled microstimulator of tibial nerve in PNP and CRPS was evaluated. In this pilot study the average preoperative visual analog scale (VAS) score in six patients was 7.5, with 1, 3 and 6 months: 2.6 ( p =0.03), 1.6 ( p =0.03), and 1.3 ( p =0.02), respectively. The mean average score in the six patients a week preceding the baseline visit was 7.96, preceding the 1, 3 and 6 month visits: 3.32 ( p =0.043), 3.65 ( p =0.045), and 2.49 ( p =0.002), respectively. The average short-form McGill pain score before surgery was 23.8, and after 1, 3 and 6 months it was 11.0 ( p =0.45), 6.3 ( p =0.043), and 4.5 ( p =0.01), respectively. Applied therapy caused a reduction of pain immediately after its application and clinical improvement was sustained on a similar level in all patients for six months. No complications of the treatment were observed. Intermittent tibial nerve stimulation by using a novel, miniature, wirelessly controlled device can be effective and feasible in PNP and CRPS. It is a safe, minimally invasive, and convenient neuromodulative method. Keywords: tibial nerve stimulation, peripheral nerve stimulation, miniature stimulator, peripheral neuropathic pain, complex regional pain syndrome
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".