Assessing the Correlation between Skeletal and Corresponding Soft-Tissue Equivalents to Determine the Relationship between CBCT Skeletal/Dental Dimensions and 3D Radiographic Soft-Tissue Equivalents
Bibliographic record
Abstract
Objective. Compare measurements of skeletal and dental areas on the CBCT to the corresponding soft-tissue measures taken from a 3D Facial Scanner. Methods. 30 patients with CBCT and 3D Facial scanner photos were selected from the orthodontic program database. 30 different distance measurements were obtained from CBCT and facial scan. OrthoInsight software was used to obtain the measurements from the facial scan images, and AVIZO software was used for corresponding CBCT landmarks. The Euclidean distance formula was used to determine the distances for the corresponding x, y, and z coordinates of the CBCT. Reliability for CBCT and Facial Scanner was completed by calculating 30 distances for 10 patients, 3 times. Once reliability was determined, all 30 distances were calculated once for CBCT and facial scanner on each patient and descriptive statistics and paired t-test were applied. Results. All distances measured presented excellent reliability, the lowest one being the left eye width for the facial scanner (ICC 0.847). The landmark with the highest mean error on the CBCT was 2.0 ± 1.6 mm on the z-axis for the spinal level landmark. The Facial Scanner’s largest mean measurement error was 1.5 ± 0.9 mm for the distance of the left corner of the mouth to gonion. All data except width between outer eye corners were statistically significant ( p<0.05 ). The average differences between facial scan and CBCT measurements ranged between 0.77 mm (left canine to cheekbone) to 26.94 mm (left subnasale to gonion) and are thus comparable. All measurements show a reasonable standard deviation between 2.57 mm (left eye width) to 9.91 mm (left gnathion to EAM). Conclusion. Distances obtained from CBCT and facial scan present mild differences giving the perspective of a relationship between them. Understanding this difference and relationship can make it plausible to expect certain underlying skeletal distances under soft-tissue structures.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".