Snoring in preschool children: prevalence, severity and risk factors
Bibliographic record
Abstract
Epidemiological data on snoring from preschool children are scarce, although habitual snoring (snoring on almost all nights) has been associated with poor long-term outcomes. In a population survey of 6,811 children aged 1-4 yrs (from Leicestershire, UK) the present authors determined prevalence, severity and risk factors for snoring, especially habitual snoring. In 59.7% of the children, parents reported snoring in the previous 12 months, including 7.9% with habitual snoring and 0.9% with habitual snoring and sleep disturbance. Prevalence of habitual snoring increased with age from 6.6% in 1-yr-olds to 13.0% in 4-yr-olds. Habitual snoring was associated with: one and both parents smoking (adjusted odds ratio (OR) 1.46 and 2.09, respectively); road traffic (OR 1.23); single parent (OR 1.60); and in White but not South Asian children, socioeconomic deprivation (OR 1.25 and 2.03 for middle and upper thirds of Townsend score, respectively). Respiratory tract symptoms related to atopic disorders and to respiratory infections were strongly associated with snoring; however, body mass index was not. In conclusion, habitual snoring is common in preschool children with one-third of cases attributable to avoidable risk factors. The strong association with atopic disorders, viral infections and environmental exposures suggests a complex aetiology, based on a general vulnerability of the respiratory tract.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".