Moderated Poster Session 4: Pediatrics, Trauma & Infertility
Bibliographic record
Abstract
Background: Dysfunctional elimination syndrome is a behavioral condition in children associated with urine holding and constipation.It may cause urinary incontinence and infection which can greatly decrease a child's or family's quality of life.This study assesses whether or not pelvic muscle retraining using biofeedback improves urinary incontinence and UTI in children with dysfunctional elimination syndrome.Methods: Retrospective review of all children ages 4 to 18 years old who presented to the Children's Hospital of Eastern Ontario (CHEO) with dysfunctional elimination syndrome and underwent biofeedback pelvic muscle retraining between 2005 and 2010.Primary outcomes were urinary incontinence and UTI.Biofeedback was performed by the urodynamics nurse.Results: There was a total of 35 patients, 15 males, median age 9 yrs (IQ 1st 7; 3rd 10.5).Mean follow up 7.7 (4.3) months.Number of patients with urinary incontinence decreased after biofeedback from 54% (19/35) to 34% (12/35) (p<0.0001).The mean number of urinary accidents per week decreased from 7.1(SD8.2) to 3.9 (SD6.4)(p<0.002).Mean dysfunctional voiding symptom score improved from 9.5 (SD4.3) to 5.7 (SD4.1)pre and post biofeedback (p<0.0001).UTI and post-void residuals were not significantly different, 13 to 8 (p=0.21) and 69 (SD79) cc to 58 (SD70) cc (p=0.35)respectively.Number of patients with constipation decreased from 11 to 4 (p=0.08),but this was not significant.Conclusions: In this retrospective analysis, biofeedback pelvic floor retraining improved incontinence in children with dysfunctional elimination syndrome.It did not, however, improved the occurrence of constipation, UTI, or decreased post-void residuals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.530 | 0.182 |
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".