Bladder and bowel dysfunction in children: An update on the diagnosis and treatment of a common, but underdiagnosed pediatric problem
Bibliographic record
Abstract
Bladder and bowel dysfunction (BBD) describes a spectrum of lower urinary symptoms (LUTS) accompanied by fecal elimination issues that manifest primarily by constipation and/or encopresis. This increasingly common entity is a potential cause of significant physical and psychosocial burden for children and families. BBD is commonly associated with vesicoureteral reflux (VUR) and recurrent urinary tract infections (UTIs), which at its extreme may lead to renal scarring and kidney failure. Additionally, BBD is frequently seen in children diagnosed with behavioural and neuropsychiatric disorders such as attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). Patients with concomitant BBD and neuropsychiatric disorders have less favourable treatment outcomes. Early diagnosis and treatment of BBD are critical to avoid secondary comorbidities that can adversely impact children's kidney and bladder function, and psychosocial well-being. The majority of patients will improve with urotherapy, adequate fluid intake, and constipation treatment. Pharmacological treatment must only be considered if no improvement occurs after intensive adherence to at least six months of urotherapy ± biofeedback and constipation treatment. Anticholinergics remain the mainstay of medical treatment. Selective alpha-blockers appear to be effective for improving bladder emptying in children with non-neurogenic detrusor overactivity (DO), incontinence, recurrent UTIs, and increased post-void residual (PVR) urine volumes. Alpha-1 blockers can also be used in combination with anticholinergics when overactive bladder (OAB) coexists with functional bladder outlet obstruction. Minimally invasive treatment with onabotulinumtoxinA bladder injections, and recently neurostimulation, are promising alternatives for the management of BBD refractory to behavioural and pharmacological treatment. In this review, we discuss clinical presentation, diagnostic approach, and indications for behavioural, pharmacological, and surgical treatment of BBD in children based on a thorough literature review. Expert opinion will be used when scientific evidence is unavailable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".