Desmopressin Withdrawal Strategy for Pediatric Enuresis: A Meta-analysis
Bibliographic record
Abstract
CONTEXT: A high relapse rate after discontinuation of desmopressin treatment of pediatric enuresis is consistently reported. Structured withdrawal strategies have been used to prevent relapse. OBJECTIVE: To assess the efficacy of a structured withdrawal strategy of desmopressin on the relapse-free rate for desmopressin responder pediatric enuresis. DATA SOURCES: Systematic literature search up to November 2015 on Medline, Embase, Ovid, Science Direct, Google Scholar, Wiley Online Library databases, and related references without language restriction. STUDY SELECTION: Related clinical trials were summarized for systematic review. Randomized controlled trials on the efficacy of structured versus abrupt withdrawal of desmopressin in sustaining relapse-free status in pediatric enuresis were included for meta-analysis. DATA EXTRACTION: Eligible studies were evaluated according to Cochrane Collaboration recommendations. Relapse-free rate was extracted for relative risk (RR) and 95% confidence interval (CI). Effect estimates were pooled via the Mantel-Haenszel method with random effect model. RESULTS: Six hundred one abstracts were reviewed. Four randomized controlled trials (total 500 subjects) of adequate methodological quality were included for meta-analysis. Pooled effect estimates compared with the abrupt withdrawal, structured withdrawal results to a significantly better relapse-free rate (pooled RR: 1.38; 95% CI, 1.17-1.63; P = .0001). Subgroup analysis for a dose-dependent structured withdrawal regimen showed a significantly better relapse-free rate (pooled RR: 1.48; 95% CI, 1.21-1.80; P = .0001). LIMITATIONS: The small number of studies included in meta-analysis represents a major limitation. CONCLUSIONS: Structured withdrawal of desmopressin results in better relapse-free rates. Specifically, the dose-dependent structured withdrawal regimen showed significantly better outcomes.
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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.051 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".