Early versus late treatment of voiding dysfunction with pelvic neuromodulation
Bibliographic record
Abstract
INTRODUCTION: Pelvic neuromodulation is an established method of treating voiding dysfunction. Little is known about the pathophysiology associated with voiding dysfunction. Reports have suggested that a delay in treating patients with sacral neuromodulation therapy can impact the success rate of this type of treatment in voiding dysfunction. We examined patient response to pelvic neuromodulation when it was applied early versus late in the postdiagnosis of voiding dysfunction. METHODS: We conducted a retrospective study of 42 patients (38 women and 4 men) with voiding dysfunction who underwent surgery for implant with the Interstim (Medtronic, Minneapolis, Minn.). Prior to implantation, patients were required to pass a percutaneous nerve evaluation (PNE) over a 1-week period. Patients were observed for 20-48 months postimplantation. All patients recorded their voiding parameters at baseline, after screening and every 6 months thereafter. Twenty patients (in the early group) underwent implant surgery with the neurostimulator 2-4 weeks post-PNE, and 22 patients (the late group) had the device implanted 6-24 months post-PNE owing to local logistical circumstances. RESULTS: In the early group, 16 of 20 patients (80%) maintained a good response. In the late group, 13 of 22 (59%) patients showed a good response. Groups were well matched in terms of age, duration of voiding dysfunction and incidence of comorbidity. CONCLUSION: Patients who were delayed more than 6 months in receiving the neurostimulator implant showed a worse response than did patients who had the device implanted soon after PNE. This indicates the possibility of disease progression, which may limit the response to sacral neuromodulation.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".