Evaluation of the palatal bone in different facial patterns for orthodontic mini-implants insertion: A cone-beam computed tomography study
Bibliographic record
Abstract
OBJECTIVE: Evaluate the height, thickness and cortical density of the palatal bone of adults with different vertical facial patterns using Cone-Beam Computed Tomography (CBCT). METHODS: This study analyzed 75 CBCTs of patients between 18 and 35 years old (45 men and 30 women). The CBCTs were classified into three groups based on their facial pattern: normodivergent, hypodivergent and hyperdivergent as determined from lateral cephalograms synthesized from the CBCTs. The height, cortical thickness and cortical density of the palatal bone were measured at 4, 8, 12, 16 and 20mm posterior to the incisive foramen, and at 3, 6 and 9mm lateral to the midpalatal suture. ANOVA with Tukey post-hoc tests were used for analysis of the data, at significance level of p< 0.05. RESULTS: The hypodivergent pattern had a significant difference and the greatest height and cortical thickness of the palatal bone, followed by the hyperdivergent and the normodivergent patterns. No significant differences were found in minimum and maximum values of cortical density. CONCLUSION: The palatal bone is a favorable anatomical area to install different orthodontic temporary anchorage devices (TADs), where individuals with the hypodivergent vertical facial pattern have a higher height and cortical thickness of the palatal bone, followed by the hyperdivergent pattern and finally the normodivergent pattern. No significant differences in the cortical density of the palatal bone in the three facial patterns were found.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".