The Use of Indomethacin for Nocturnal Enuresis in Children With Nephrogenic Diabetes Insipidus: A Case Report and Review of the Literature
Bibliographic record
Abstract
Rationale: Nocturnal enuresis is a common symptom of nephrogenic diabetes insipidus (NDI) in children. Published reports on the treatment of nocturnal enuresis in this population are scarce. Presenting concern of the patient: 2 brothers, aged 8 and 9 years, presented for outpatient pediatric nephrology follow-up. Despite being medically stable on their current medication regimen, they both experienced significant distress due to primary nocturnal enuresis, including decreased self-esteem and social stress. Diagnoses: The brothers had primary nocturnal enuresis related to their high urine output from NDI. Interventions: we describe a case of 2 brothers with NDI in whom indomethacin was added to their pharmacologic treatment specifically to address enuresis. Outcome: Both brothers experienced a significant decrease in the frequency of nocturnal enuresis and improvement in their perceived quality of life. Teaching points: Nocturnal enuresis is a bothersome symptom of NDI with adverse psychological effects. Indomethacin can improve nocturnal enuresis in some patients. Treatment with nonsteroidal anti-inflammatory drugs carries a risk of gastrointestinal and kidney side effects. We advocate for a patient-centered approach to the treatment of NDI which includes optimizing both the medical and the psychological needs of 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".