Urinary Outcomes in Patients with Down's Syndrome and Hirschsprung's Disease
Bibliographic record
Abstract
Introduction Previous research in children with Hirschsprung's disease (HD) and Down's syndrome (DS) has focused on colorectal outcomes. We set out to review urinary outcomes in this patient group. Materials and Methods The medical records of all patients aged five years and older with HD were reviewed, and patients and caregivers filled out the Vancouver Symptom Score at intake, which is designed and validated to diagnose dysfunctional elimination syndrome. Results A total of 104 patients with HD were included in this study. Of these, 16 (15%) patients had DS. There were no significant differences in the prevalence of enterocolitis or colorectal symptoms between patients with or without DS. Five of 88 (6%) patients without DS and 7 of 16 (44%) (p = 0.00001) with DS reported having urinary accidents. Patients with HD and DS scored higher on the Vancouver score (9 vs. 17.5; p = 0.007), indicating more severe urinary symptoms. Patients who also reported fecal accidents scored significantly higher on the Vancouver (12 vs. 9; n = 61; p = 0.016), indicating more problems. Conclusion Patients with DS appear to be a unique subset of HD patients who have a higher prevalence of urinary symptoms after surgery. In the postoperative care of patients with HD and DS, a strong focus should be placed on postoperative urinary care in addition to their bowel care. This could significantly ease care and contribute to the quality of life of the parents and the patient.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".