Incisors inclination in relation to lip parameters: a CBCT study
Bibliographic record
Abstract
INTRODUCTION: With the introduction of Cone Beam Computed Tomography (CBCT) in dentistry, precise measurements are now attainable. OBJECTIVE: This study seeks to explore the correlation between incisors inclination and various lip parameters utilizing CBCT technology. Moreover, it aims to assess whether specific incisors inclinations significantly influence lip thickness, length, and position. MATERIAL AND METHODS: This was a retrospective observational study of available records of orthodontic patients (n=84) aged between 11 and 17.5 years old with pre- and post-treatment CBCT imaging. The 3D Slicer software was used to assess lip parameters and incisors inclinations while adhering to standard CBCT imaging methods. Statistical analysis was conducted to find associations between incisors inclination and lip parameters. RESULTS: Within certain limits of incisor inclination, lip parameters showed minimal impact. Changes in upper incisor inclination within an average of 5° did not significantly correlate with upper lip parameters. Similarly, changes in lower incisor inclination within an average of 5.6° had no significant effect on lower lip parameters. However, inclination changes of tooth #21 within 5.4° significantly affected upper lip length within 0.35mm. CONCLUSIONS: Lip parameters remained unaffected with specific ranges of incisor inclinations, except for a slight effect on upper lip length with changes in inclination of tooth #21. Clinicians can consider the specific ranges of incisors inclination during treatment planning, especially for patients concerned about lip appearance.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".