Nasal Septum Changes in Adolescents Treated with Tooth-Borne and Bone-Borne Rapid Maxillary Expansion: A CBCT Retrospective Study Using Skeletal Tortuosity Ratio and Deviation Analysis
Bibliographic record
Abstract
Background: Using three-dimensional (3D) images, this study evaluated the impact of Rapid Maxillary Expansion (RME) on changes in Nasal Septal Deviation (NSD). Methods: Cone-beam computed tomography (CBCT) scan of 40 children with transverse maxillary deficiency, who received tooth-borne (TB) RME or bone-borne (BB) RME, were included in this investigation. Two CBCT scans were performed: one before to appliance installation (T0) and one after a 6-month retention period (T1). The analysis was performed by dividing the actual length of the septum by the desired length in the mid-sagittal plane to measure NSD based on the tortuosity ratio (TR). Results: Subjects in the TB group showed a statistically significant reduction (p < 0.05) of the TR value from T0 to T1, according to the paired Student t test. Subjects in the BB group showed similar findings, with a statistically significant reduction (p < 0.05) of the TR value from T0. No statistically significant differences were found between the mean changes of TR between TB group and BB group. Conclusions: RME may have some effects in reducing the degree of NSD; however, no differences were found between RME performed with TB and BB anchorage systems.
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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 | 0.000 | 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".