Prevalence of Altered Craniofacial Morphology in Children With <scp>OSA</scp>
Bibliographic record
Abstract
Snoring and obstructive sleep apnoea (OSA) affect a significant percentage of children. Recent studies have suggested that altered craniofacial morphology may contribute to the multifactorial pathophysiology of OSA. This study aims to determine the prevalence of craniofacial abnormalities and malocclusion in children referred for polysomnography due to OSA suspicion. This is a multicentre prevalence study completed across four Canadian sites. Otherwise, healthy children (≥ 4 years old) who were seen at the sleep clinic were recruited. Upon arrival for their hospital-based overnight sleep recording, a clinical orthodontic assessment and a series of paediatric sleep questionnaires were completed for each participant. Data from 315 children (age 9.37 ± 3.70) revealed significant risk factors associated with the presence of OSA, including male sex, presence of snoring, endomorph body type, and hypertrophic tonsils. The intra-oral and facial morphologic characteristics were not significantly different between children with (AHI 9.51 ± 10.94) and without (AHI 0.84 ± 0.50) PSG-verified OSA. Factors such as maxillary constriction/posterior crossbite and a retrognathic mandible showed similar (p > 0.05) prevalence between groups. Hierarchical regression analysis showed no statistically significant facial and dental variables in predicting AHI. In conclusion, a multidisciplinary approach involving dental professionals with expertise in growth and development is crucial for the assessment of possible craniofacial abnormalities in children with OSA. Craniofacial morphology may play a limited role in the pathophysiology of OSA in most children, as no differences in the prevalence of these variables in children with and without OSA were found in this large, multicentre study.
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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 | 0.001 | 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) | 0.001 | 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".