Changes in Upper Airway Airflow After Rapid Maxillary Expansion Beyond the Peak Period of Adenoidal Growth—A CBCT Study Using Computer Fluid Dynamics and Considering Adenoidal Dimensions as a Factor
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: This retrospective study used computer fluid dynamics (CFD) to evaluate the medium-term changes in the upper airways (UA) airflow after rapid maxillary expansion (RME) in three age-matched groups with different degrees of adenoidal obstruction. METHODS: The sample included Cone-Beam Computed Tomography (CBCT) of 67 adolescents taken before (T0) and 12 months after RME (T1) and divided into three cohorts: Control Group (CG, <25% obstruction: 24 subjects, mean age = 11.8 ± 1.3), Adenoids Group 1 (AG1, >25% <75% obstruction: = 22 subjects, mean age = 10.9 ± 1.5), Adenoids Group 2 (AG2, >75% obstruction: = 21 subjects, mean age = 11.2 ± 1.6). The airflow pressure, velocity and obstruction were simulated using computer fluid dynamics (CFD). RESULTS: The pressure significantly improved in CG and AG1 groups while the velocity improved in AG1 as well as the prevalence of obstruction improvement. The airflow pressure and velocity changes could be attributed to the reduction of the resistances in the adenotonsillar region, which was remarkably more marked in the AG1. CONCLUSIONS: Alterations in the adenotonsillar region likely represent the most substantial factors influencing airflow changes after RME. The integration of anatomical and functional data, along with the identification of baseline patient characteristics, may facilitate the characterization of phenotypes most appropriate for initial management through either Rapid Maxillary Expansion (RME) or otolaryngologic (ENT) interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".