Recruitment of unmyelinated C-fibers mediates the bladder-inhibitory effects of tibial nerve stimulation in a continuous-fill anesthetized rat model
Bibliographic record
Abstract
Although percutaneous tibial nerve stimulation is considered a clinically effective therapy for treating overactive bladder, the mechanism by which overactive bladder symptoms are suppressed remains unclear. The goal of the present study was to better understand the role of specific neural inputs (i.e., fiber types) on the bladder-inhibitory effects of tibial nerve stimulation (TNS). In 24 urethane-anesthetized rats, a continuous suprapubic saline infusion model was used to achieve repeated filling and emptying of the bladder. A total of 4 TNS trials (pulse frequency: 5 Hz) were applied in randomized order, where each trial used different amplitude settings: 1) no stimulation (control), 2) Aβ-fiber activation, 3) Aδ-fiber activation, and 4) C-fiber activation. Each stimulation trial was 30 min in duration, with an intertrial washout period of 60−90 min. Our findings showed that TNS evoked statistically significant changes in bladder function (e.g., bladder capacity, residual volume, voiding efficiency, and basal pressure) only at stimulation amplitudes that electrically recruited unmyelinated C-fibers. In a subset of experiments, TNS also resulted in transient episodes of overflow incontinence. It is noted that changes in bladder function occurred only during the poststimulation period. The bladder-inhibitory effects of TNS in a continuous bladder filling model suggests that electrical recruitment of unmyelinated C-fibers has important functional significance. The implications of these findings in percutaneous tibial nerve stimulation therapy should be further investigated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".