A modified approach to patient’s selection with improved clinical outcomes in sacral nerve modulation
Bibliographic record
Abstract
INTRODUCTION: Since the marketing of the percutaneous permanent tined leads (PPTL), many centres rely solely on these instead of the percutaneous nerve evaluation (PNE) as a screening tool. At our centre, we routinely perform PNE. Moreover, with our limited hospital resources, we have adopted a stricter definition of success in the patient selection process using an improvement of more than 60% as a cut-off point. This study presents our experience with sacral nerve stimulation using PPTL as an adjunct to PNE to improve the outcome of the screening method for patients suffering from refractory voiding dysfunction. METHODS: We reviewed the charts of 106 patients who underwent a PNE between 2001 and 2008. The outcome of the procedures, the complication rates and its long-term effect were reviewed. RESULTS: Overall, 116 PNE were performed and it was successful in 54%. Forty-five out of the 62 patients with a successful PNE underwent the stage I procedure. Of these, 93% had a successful stage I and were later implanted with the implantable pulse generator (IPG). The remaining 12 patients underwent the simultaneous implantation of the PPTL and IPG using the open procedure and it was successful in 10 of them. CONCLUSION: The PNE is a good adjunct to the staged procedure to select the appropriate candidates for sacral nerve stimulation, especially with limited resources.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".