Condylar volume changes in class II division 2 cases treated with unlocking the mandible using clear aligners
Bibliographic record
Abstract
INTRODUCTION: Class II Division 2 malocclusion involves retroclined maxillary incisors and deep overbite, often accompanied by mandibular retrusion. "Unlocking" the mandible by proclining the maxillary incisors, correcting the deep bite, and expanding the maxillary arch has been used to treat this malocclusion. However, the impact of this treatment, using Invisalign® clear aligners, on condylar volume remains unclear. This study evaluates three-dimensional changes in condylar volume after using Invisalign® to unlock the mandible in Class II Division 2 growing patients. METHODS: Cone-beam computed tomography (CBCT) data were collected from 22 adolescent patients (11 in the treatment group; 11 in the control group) at T1 (pre-treatment) and T2 (1.5-2 years post-T1). Dolphin imaging software was used for cephalometric tracing, while 3D Slicer and ITK-SNAP software calculated condylar volume. Repeated measure ANOVA compared condylar volume changes, and Pearson's Correlation Coefficient assessed the relationship between condylar volume change and ANB angle in the treatment group. RESULTS: Both groups showed significant condylar volume increases between T1 and T2 (treatment: P < 0.001, 127.45 ± 30.97, control: P = 0.015, 98.8 ± 36.31), with no significant difference between groups at T1 (P = 0.289, 89.19 ± 81.2) or T2 (P = 0.167, 117.9 ± 81.7). The change in ANB angle did not correlate with the condylar volume increase in the treatment group (Pearson's R = -0.15, P = 0.681). CONCLUSION: Unlocking the mandible successfully corrected Class II Division 2 malocclusion, but condylar volume increases in both groups were likely due to normal growth rather than treatment. Condylar volume change was not correlated with malocclusion correction.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".