Changes in upper airway airflow after rapid maxillary expansion considering normal craniofacial development as a factor: a retrospective study using computer fluid dynamics
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Evidence suggests nasal airflow resistance reduces after rapid maxillary expansion (RME). However, the medium-term effects of RME on upper airway (UA) airflow characteristics when normal craniofacial development is considered are still unclear. This retrospective cohort study used computer fluid dynamics (CFD) to evaluate the medium-term changes in the UA airflow (pressure and velocity) after RME in two distinct age-based cohorts. MATERIALS/METHODS: The study included 48 subjects who underwent RME divided into two cohorts: a 6-9-year-old group (EEG group: early expansion group - 25 subjects) and an 11-14-year-old group (LEG group: late expansion group - 23 subjects). The nasal cavity and pharyngeal anatomy were segmented from Cone-beam computed tomography reconstructions taken before RME (T0) and 12 after RME (T1). The two UA airflow variables (pressure and velocity) were simulated using CFD. The maxillary expansion (PW) amount, two cross-sectional area measurements (CS1 = anterior cross-section and CS2 = posterior cross-section), and four UAs' subregions (NC = nasal cavity, PAtotal = pharyngeal airway, NP = nasopharynx, VP = velopharynx, and OP = Oropharynx) were also considered. All data were statistically analyzed. RESULTS: At the baseline, the airflow pressure, velocity, and noted obstructions were significantly higher in the EEG compared to LEG. At T1, there was a significant improvement in the median airway parameters in both groups, which was remarkably greater in the EEG. A significant negative correlation was found between pressure/velocity and both CS2 and NP. According to the CFD plots, the airflow pressure and velocity changes could be attributed to the reduction of the adenotonsillar tissues' sizes, which were remarkably more marked in the EEG. LIMITATIONS: The results of this study cannot be generalized since they referred to a retrospective orthodontic sample without obstructive adenotonsillar hypertrophy. CONCLUSIONS/IMPLICATIONS: Twelve months after RME, normal craniofacial developmental changes and spontaneous adenotonsillar tissues volume regression could represent the most significant factors influencing UA airflow changes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".