Identification of obstructive sleep apnea in children with obesity: A cluster analysis approach
Bibliographic record
Abstract
BACKGROUND: Obstructive sleep apnea (OSA) is a heterogeneous disorder with a prevalence of 25%-60% in children with obesity. There is a lack of diagnostic tools to identify those at high risk for OSA. METHOD: Children with obesity, aged 8-19 years old, were enrolled into an ongoing multicenter, prospective cohort study related to OSA. We performed k-means cluster analysis to identify clinical variables which could help identify obesity related OSA. RESULTS: In this study, 118 participants were included in the analysis; 40.7% were diagnosed with OSA, 46.6% were female and the mean (SD) body mass index (BMI) and age were 39.7 (9.6) Kg/m², and 14.4 (2.6) years, respectively. The mean (SD) obstructive apnea-hypopnea index (OAHI) was 11.0 (21.1) events/h. We identified two distinct clusters based on three clustering variables (age, BMI z-score, and neck-height ratio [NHR]). The prevalence of OSA in clusters 1 and 2, were 22.4% and 58.3% (p = 0.001), respectively. Children in cluster 2, in comparison to cluster 1, had higher BMI z-score (4.7 (1.1) versus 3.2 (0.7), p < 0.001), higher NHR (0.3 (0.02) versus 0.2 (0.01), p < 0.001) and were older (15.0 (2.2) versus 13.7 (2.9) years, p = 0.09), respectively. However, there were no significant differences in sex and OSA symptoms between the clusters. The results from hierarchical clustering were similar to k-means analysis suggesting that the resulting OSA clusters were stable to different analysis approaches. INTERPRETATION: BMI, NHR, and age are easily obtained in a clinical setting and can be utilized to identify children at high risk for OSA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".