Obstructive sleep apnea screening in children with asthma
Bibliographic record
Abstract
RATIONALE: Obstructive sleep apnea is highly prevalent in children with asthma, particularly in obese children. The sleep-related breathing disorder screening questionnaire has low screening accuracy for obstructive sleep apnea in children with asthma. Our goal was to identify the questions on the sleep-related breathing disorder survey associated with obstructive sleep apnea in children with asthma. METHODS: Participants completed the survey, underwent polysomnography and their body mass index z-score was measured. Participants with survey scores above 0.33 were considered high risk for obstructive sleep apnea and those with an apnea-hypopnea index ≥ 2 events/h classified as having obstructive sleep apnea. Logistic regression was used to examine the association of each survey question and obstructive sleep apnea. Positive and negative predictive values were calculated to estimate screening accuracy. RESULTS: The prevalence of obstructive sleep apnea was 40% in our sample (n = 136). Loud snoring, morning dry mouth, and being overweight were the survey questions associated with obstructive sleep apnea. The composite survey score obtained from all 22 questions had positive and negative predictive values of 51.0% and 65.5%, while the combined model of loud snoring, morning dry mouth, and being overweight had positive and negative predictive values of 60.3% and 77.6%. On the other hand, the body mass index z-score alone had positive and negative predictive values of 76.3% and 72.2%. CONCLUSIONS: The body mass index z-score is useful for obstructive sleep apnea screening in children with asthma and should be applied routinely given its simplicity and concerns that obstructive sleep apnea may contribute to asthma morbidity.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".