Personal Characteristics of Enuretic Children: An Epidemiological Study from South-East Europe
Bibliographic record
Abstract
INTRODUCTION: This study examined the personal characteristics of enuretic children and investigated the risk factors of nocturnal enuresis among schoolchildren. METHODS: It was a cross-sectional and descriptive questionnaire study and 2,000 children were stratified according to school population, age and gender. The questionnaire was designed for parents to collect information about the prevalence and associated factors as well. RESULTS: Nocturnal enuresis was reported in 159 cases (9.8%). The parameters of bladder control after 2 years of age, urination more than 5 times a day, urinary infection history, history of psychological or physical trauma, siblings with health problems, large family size, lack of a private bedroom, and constipation were more frequent in enuretics (p < 0.05). The parameters of having fecal incontinence, parents and siblings with nocturnal enuresis, low educational level of the mother and poor school performance seem to be risk factors for nocturnal enuresis. However, the parental concern level was high, approximately half of the enuretic children did not visit a physician for management of the problem. CONCLUSION: Nocturnal enuresis could be a multifactorial problem originating from bladder dysfunction, deranged sleep patterns and psychological and hereditary predisposition. Hereditary disposition and having fecal incontinence may be important risk factors for enuresis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".