Improvement in Bladder Function in Children With Functional Constipation After a Bowel Management Program
Bibliographic record
Abstract
Abstract Purpose We sought to determine if children with functional constipation (FC) would have an improvement in bladder function with treatment of constipation with a bowel management program (BMP). Methods A single-institution review was performed in children aged 3-18 with FC who underwent a BMP from 2014-2020. Clinical characteristics, bowel management details, and the Vancouver Symptom Score for Dysfunctional Elimination Syndrome (VSS), Baylor Continence Scale (BCS), and Cleveland Clinic Constipation Score (CCCS) were collected. Data were analyzed using linear mixed effect modeling with random intercept.Results 241 patients were included with a median age of 9 years. Most were White (81%) and 47% were female. Univariate tests showed improvement in VSS (-3.6, P<0.0001), BCS (-11.96, P<0.0001), and CCCS (-1.9, P<0.0001) among patients having undergone one BMP. Improvement was noted in VSS and CCCS among those with more than one BMP (VSS: -1.66, P=0.023; CCCS: -2.69, P<0.0001). Multivariate tests indicated undergoing a BMP does result in significant improvement in VSS, BCS, and CCCS (P<0.0001).Conclusions There is significant improvement in bladder function in children with FC who undergo a BMP. For patients with bowel and bladder dysfunction and FC, a BMP is a reasonable treatment strategy for lower urinary tract symptoms.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".