Rapid Palatal Expansion Effects on Nasal Airway Dimensions as Measured by Acoustic Rhinometry
Bibliographic record
Abstract
OBJECTIVE: To evaluate available information on the effects of rapid maxillary expansion on nasal airway minimal cross-sectional area and volume, as measured by acoustic rhinometry. MATERIALS AND METHODS: An electronic database search was conducted. Based on abstracts/titles, articles were initially selected; then full articles were retrieved and were further sorted according to secondary, more stringent criteria. References from selected articles were hand-searched for potential missed publications. Clinical trials using acoustic rhinometry on subjects undergoing rapid maxillary expansion therapy were included. Syndromic or medically compromised patients and absence of an untreated control group were reasons for exclusion. Selected studies thereafter were evaluated methodologically. RESULTS: Only four articles reached final selection, and their overall methodology scores were low, limiting the applicability of results. After rapid maxillary expansion, three of four studies found statistically significant increases in minimal cross-sectional area, and two of three studies reported statistically significant increases in nasal cavity volume as compared with control groups. It appears that any increase is less stable if a traditional technique is used on patients who have passed their peak growth spurt. CONCLUSIONS: Although some increases in nasal dimensions have been reported, the changes in nasal volume were small and should not be presented to patients as a clinically significant indication for therapeutic maxillary expansion.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".