Sacral Neuromodulation in Patients With a Cardiac Pacemaker
Bibliographic record
Abstract
The objective of this study was to describe our experience using sacral neuromodulation to treat urinary urgency, frequency, urge incontinence, and chronic urinary retention in patients with cardiac pacemakers. With the increasingly widespread use of InterStim for bladder function restoration, we are seeing more complex patients with multiple comorbidities, including cardiac conditions. Herein, we report 3 cases of individuals with cardiac pacemakers who underwent InterStim implantation to treat urinary conditions. This study is a case series of 3 patients with cardiac pacemakers who underwent sacral neuromodulation to treat refractory voiding dysfunction. The initial patient screening for InterStim therapy involved percutaneous nerve evaluation (PNE), in which a temporary untined lead wire was placed through the S3 foramen. Patients who did not respond to PNE proceeded to a staged implant. All patients in this study had a greater than 50% improvement of their urinary symptoms during the initial trial and underwent placement of the InterStim implantable pulse generator (IPG). Postoperative programming was done under electrocardiogram monitoring by a cardiologist. No interference was observed between the Inter- Stim IPG and the cardiac pacemaker. In this group of patients, sacral neuromodulation in the presence of a cardiac pacemaker appears to have been safe.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".