The Effect of Upper Arch Expansion by Clear Aligners on Nasal Airway Volume in Children: A Preliminary Study
Bibliographic record
Abstract
Adjustments to the anatomy of the facial region, such as maxillary expansion, may impact the geometry of the nasal airway and may increase nasal airway volume. The purpose of this study was to investigate the possible effect of maxillary dentoalveolar expansion using clear aligners on the nasal airway’s volume and intermolar distance in pediatric patients. Before and after maxillary expansion treatment using clear aligners, cone-beam computed tomography (CBCT) radiographs were taken as part of the diagnostic and progress records of 11 children (6–13 years) with constricted maxilla (the experimental group). The CBCT scans of 7 children (7–12 years) who had no treatment were considered to be the control group. The changes in nasal airway volume and intermolar distance between the experimental and control groups were compared and analyzed. Correlation analysis between nasal airway volume and intermolar distance changes was also performed. Compared with the control group, the nasal airway volume of the patients in the experimental group showed a significant increase (1595.6 ± 804.1 mm3; p < 0.001), and the intermolar distance also increased significantly (2.4 ± 0.4 mm; p < 0.001). However, there was little correlation between the change in intermolar distance and the change in nasal airway volume in the experimental group (r = −0.029) and a negative correlation in the control group (r = −0.768). This study showed increased maxillary intermolar width and increased nasal airway volume in children with constricted maxilla who underwent orthodontic maxillary expansion using clear aligners. Further studies with larger sample sizes and long follow-ups are needed. Due to the study design and small sample size, the results should be interpreted with caution and no causal relationship can be drawn between maxillary expansion using clear aligners and obstructive sleep apnea.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".