Obstructive sleep apnea in children aged 3 years and younger: Rate and risk factors
Bibliographic record
Abstract
OBJECTIVE: Undiagnosed and untreated obstructive sleep apnea (OSA) can predispose children to neurobehavioural consequences. However, there is a lack of data identifying rate of, and risk factors for, OSA in very young healthy children. The objective of this study was to determine the rate of OSA and identify risk factors associated with the presence and severity of OSA in children aged 3 years and younger. METHODS: This was a retrospective chart review of healthy children between 1 and 3 years old who had a baseline polysomnogram (PSG) between January 2012 and June 2017. Patient demographics, referral history, and PSG data were recorded. RESULTS: One hundred and thirteen children were referred for a PSG, of which 66 (58%) were diagnosed with OSA and 47 (42%) did not have OSA. In the OSA group, 13 (20%) were mild and 53 (80%) were moderate-severe. Nasal congestion (P=0.001), adenoid hypertrophy (P=<0.001), and tonsillar hypertrophy (P=0.04) reported at the time of referral were more common in the OSA group compared to the no-OSA group. Binary logistic regression analysis showed that referral from an otolaryngologist (odds ratio=2.6, 95% confidence interval=1.1 to 6.0) were associated with moderate-severe OSA. CONCLUSION: A high rate of OSA was found among children aged 3 years and younger. Children referred by an otolaryngologist are more likely to be diagnosed with moderate-severe OSA. Children aged 3 years and younger with symptoms of OSA should be considered high-risk for OSA and be prioritized for early PSG and management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".