Helping patients with voiding dysfunction: What are our current options?
Bibliographic record
Abstract
atients with voiding dysfunction, such as overactive bladder (OAB) syndrome and non-obstructive retention, who have failed conservative treatment are candidates for sacral nerve stimulation (SNS).Sacral nerve stimulation was originally described by Tanagho and Schmidt. 1 Medtronic received United States Food and Drug Administration (FDA) approval for Interstim in July 1998 for urge incontinence.To proceed with full implant, patients were initially tested with a percutaneous nerve evaluation (PNE).The percutaneous permanent tined lead (PPTL) was released in 2002 and is now the most commonly used procedure performed prior to a patient receiving a permanent implant. 2 The advantage of the PPTL over PNE is higher implantation rates 88% versus 46%. 3 Richard and colleagues used an interesting approach looking at the PNE as an adjunct to the PPTL and the implanted pulse generator implant.They performed a retrospective chart review of 106 patients who underwent 116 PNEs.Of these 116 patients, 62 (53%) had a successful PNE.Of these successful cases, 57 (92%) went on to an implant.Using the PNE allowed them to pre-select patients for PPTL, which increased their overall success.4 The authors address noteworthy considerations in their approach to SNS.The most worthwhile is the use of a 60% goal for symptom improvement compared to the standard 50%, which was an initial arbitrary goal.Some may argue we should consider an even higher improvement rate, possibly 70%.The authors astutely point out that the placebo success rate for anticholinergics can be as high as 60%, clearly explaining why some patients do not improve even after a full implant.Considering the significant expense of the product, elimination of the placebo effect would decrease
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".