How I Do It: Transcutaneous tibial nerve stimulation TENSI+ system.
Bibliographic record
Abstract
Overactive bladder (OAB) is a common condition that significantly impacts the quality of life (QoL), well-being and daily functioning for both men and women. Among various treatments, peripheral tibial nerve stimulation (PTNS) emerges as an effective third-line treatment for OAB symptoms, with options for either a percutaneous approach (P-PTNS) or by transcutaneous delivery (T-PTNS). Recent studies have shown negligible differences between P-PTNS and T-PTNS efficacy in alleviating urinary urgency and frequency and QoL improvement and, overall no difference in efficacy over antimuscarinic regimens. The TENSI+ system offers a cutting-edge transcutaneous approach, allowing patients to self-administer treatment conveniently at home with electrical stimulation delivery through surface electrodes. It stands out for its ease of preparation, tolerability, and high levels of patient satisfaction. Prospective multicentric data highlights TENSI+ to be an effective and safe treatment for lower urinary tract symptoms with high treatment adherence at 3 months. This paper aims to familiarize readers with the TENSI+ system, current studies, device assembly, operation, and treatment recommendations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.020 |
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".