Biofeedback as single first-line treatment for non-neuropathic dysfunctional voiding in children with diurnal enuresis
Bibliographic record
Abstract
INTRODUCTION: Non-neurogenic dysfunctional voiding (NDV) accounts for a significant portion of pediatric urology outpatient clinic visits. Biofeedback (BF) is a promising, non-invasive modality for treating children with DV and daytime wetting. Our objective was to investigate BF's efficacy as a single first-line treatment for children with NDV and diurnal enuresis. METHODS: A retrospective cohort study was conducted with a total of 61 consecutive patient records from January 2009 to March 2016. All children with NDV who had BF as first-line treatment were included. Full urological histories, physical examinations, dysfunctional voiding symptom score (DVSS), urine analysis, ultrasound (US), and uroflowmetry (UFM), and electromyogram (EMG) were performed and recorded for all patients before and after finishing the last BF cycle. The patient's satisfaction scale was also obtained. RESULTS: The mean age was 10±2.6 years. Most patients (80.3%) were females. The presenting symptoms were diurnal enuresis, urinary tract infections, and voiding discomfort in 52 (85.2%), 16 (26.2%), and 38 (62.3%) patients, respectively. Six months after the last BF cycle, there was a statistically significant objective improvement in US and UFM+EMG findings with the disappearance of EMG signals in 40 of 61 (65.5%) patients. There was also a significant subjective symptomatic improvement, as the mean DVSS had decreased from 14 to 7.9 (p=0.003). Forty-seven patients (77%) were satisfied, while only eight (13.1%) were not. CONCLUSIONS: BF is considered a potentially effective, single first-line treatment modality for children with DV and diurnal enuresis. Long-term outcome assessments are needed to assess the children's compliance and symptom recurrence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".