Comparison of soft tissue facial changes in patients 7–11 years of age with and without maxillary expansion utilizing CBCTs and 3D facial scans: A preliminary study
Bibliographic record
Abstract
BACKGROUND: The objectives of this study are to evaluate the effects of maxillary expansion over a period of 12 months on facial soft tissue measurements in children aged 7-11 years with a maxillary transverse deficiency of at least 5mm or bilateral posterior crossbite, utilizing both CBCTs and 3D facial scans, by comparison to a control group. MATERIAL AND METHODS: Data was collected from 32 patients and consisted of two groups: control and treatment (Hyrax expansion via RME, 1 turn/day). Each patient in each group underwent CBCTs, 3D facial scans and hand-wrist radiographs at two time points: pre-treatment (T0), and after the completion of expansion at post-retention (T1, 12 months). CBCTs were assessed using 3D Slicer software and 3D facial scans were assessed using OrthoInsight 3D software. The soft tissue measurements evaluated included the following: alar width, alar base width, mouth width, philtrum width, nasal tip prominence, nasolabial angle, upper lip to E-line, lower lip to E-line, upper lip height, height of vermillion of upper lip, lower lip height, height of nose, lower facial height and intercanthal width. Statistical analysis included intra- and inter-rater variability, measurement error calculation and MANOVA tests. RESULTS: From a total of 32 patients with two sets of imaging records, no statistically significant differences were found between the two groups over the one-year observation. However, when comparing the two modalities utilized in this study (CBCT imaging and 3D facial scanning), the correlation was not as optimal for specific outcome variables such as alar base width and intercanthal width, potentially due to anatomic, imaging protocols and patient related factors. CONCLUSION: The findings of this study suggest that the children in both groups experienced similar facial soft tissue changes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".