A finite element modeling study of peripheral nerve recruitment by percutaneous tibial nerve stimulation in the human lower leg
Bibliographic record
Abstract
Percutaneous tibial nerve stimulation (PTNS) is a clinical therapy for treating overactive bladder (OAB), where an un-insulated stainless steel needle electrode is used to target electrically the tibial nerve (TN) in the lower leg. Recent studies in anesthetized animals not only confirm that bladder-inhibitory reflexes can be evoked by stimulating the TN, but this reflex can also be evoked by stimulating the adjacent saphenous nerve (SAFN). Although cadaver studies indicate that the TN and major SAFN branch(es) overlap at the location of stimulation, the extent to which SAFN branches are co-activated is unknown. In this study, we constructed a finite element model of the human lower leg and applied a numeric axon model (MRG model) to simulate the electrical recruitment of TN and SAFN fibers during PTNS. The model showed that up to 80% of SAFN fibers (located at the level of the needle electrode) can be co-activated when electrical pulses are applied at the TN activation threshold, the standard therapeutic amplitude. Both the location of the inserted electrode and stimulation amplitude were important variables that affected the recruitment of SAFN branches. This study suggests further work is needed to investigate the potential therapeutic effects of SAFN stimulation in OAB patients.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".