Patients with Sacral Neuromodulation: What Are the Factors Affecting Their Therapy Satisfaction?
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: Technology has brought about changes to the sacral neuromodulation (SNM) therapy and has rendered it as a minimally invasive procedure. The presence of tined lead, smaller implantable pulse generator, and the different available patient programmers are the factors new modifications this procedure; however, no enough data are available about patient satisfaction comparing these different parameters. We therefore conducted our study among SNM-implanted patients, assessing factors affecting long-term satisfaction. PATIENTS AND METHODS: We conducted a cross-sectional study with a survey questionnaire for all SNM-implanted patients from December 2011 to May 2012. The survey questionnaire included information on patient demographics, level of satisfaction, and the need for combined medical therapy for symptom control, type of programmer used by patient, and their ability to use it efficiently. RESULTS: A total of 117 patients visited the clinic in that duration, and 71 patients filled up the survey questionnaire, with a response rate of 60.7%, diagnosis OAB 41 (57.7%), chronic non-obstructive retention 15 (21.1%) and frequency-urgency syndrome 15 (21.1%). Forty-nine (69%) of patients were on SNM therapy alone, while 31% patients needed other forms of therapy for symptoms control. The types of patient programmers used were old programmers in 10 (14.1%), new programmers in 44 (62%), and twin programmers in 4 patients. Patient SNM therapy satisfaction level was 95.8%, while 3 patients (4.2%) were not satisfied. SNM therapy satisfaction showed that it was correlated with the need for combined therapy for symptoms control (correlation coefficient 0.341, p value = 0.004), but there was no correlation with age, duration of therapy, complication, or programmer type. CONCLUSION: SNM satisfaction in patients is mainly correlated with the need for additional medications for symptom control.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".