Bladder and Bowel Dysfunction Network: Improving the Management of Pediatric Bladder and Bowel Dysfunction
Bibliographic record
Abstract
Introduction: Lower urinary tract symptoms with constipation characterize bladder and bowel dysfunction (BBD). Due to high referral volumes to hospital pediatric urology clinics and time-consuming appointments, wait times are prolonged. Initial management consists of behavioral modification strategies that could be accomplished by community pediatricians. We aimed to create a network of community pediatricians trained in BBD (BBDN) management and assess its impact on care. Methods: We distributed a survey to pediatricians, and those interested attended training consisting of lectures and clinical shadowing. Patients referred to a hospital pediatric urology clinic were triaged to the BBDN and completed the dysfunctional voiding symptom score and satisfaction surveys at baseline and follow-up. The Bristol stool chart was used to assess constipation. Results were compared between BBDN and hospital clinic patients. Results: Surveyed pediatricians (n = 100) most commonly managed BBD with PEG3350 and dietary changes and were less likely to recommend bladder retraining strategies. Baseline characteristics were similar in BBDN (n = 100) and hospital clinic patients (n = 23). Both groups had similar improvements in dysfunctional voiding symptom score from baseline to follow-up (10.1 ± 4.2 to 5.6 ± 3.3, P = 0.01, versus 10.1 ± 4.2 to 7.8 ± 4.5, P = 0.02). BBDN patients waited less time for their follow-up visit with 56 (28–70) days versus 94.5 (85–109) days for hospital clinic patients (P < 0.001). Both groups demonstrated high familial satisfaction. Conclusions: Community pediatricians may require more knowledge of management strategies for BBD. Our pilot study demonstrates that implementing a BBDN is feasible, results in shorter wait times, and similar improvement in symptoms and patient satisfaction than a hospital pediatric urology clinic.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".