Cone beam computed tomography analysis of anterior open bite management using clear aligners: a single-arm retrospective study
Bibliographic record
Abstract
Lateral cephalograms have the inherent drawback of superimposition of bilateral structures, while landmarks are more reproducible on CBCT scans. Yet no studies in the literature have utilized 3D imaging to investigate the effects of clear aligners on anterior open bite. Therefore, The aim is to measure the skeletal and dental changes that contribute to anterior open bite closure with clear aligner therapy on CBCT scans. It is a single-arm retrospective study that included 40 cases of anterior open bite who were treated using Invisalign. Pre- and post-treatment CBCT scans were traced to record 13 dental and 3 skeletal measurements. A paired t-test was conducted to compare the mean values of pre- and post-treatment measurements. Combined intrusion of the maxillary right and left molars was statistically significant, meanwhile mandibular molars maintained their vertical position. Maxillary incisors were extruded and retroclined significantly, whereas mandibular incisors were only extruded. While anterior facial height was decreased insignificantly, both lower anterior facial height and mandibular plane angle showed a significant decrease. Clear aligner (Invisalign) therapy is effective in the management of anterior open bite through vertical control, maxillary molars intrusion, maxillary incisors extrusion, maxillary and mandibular incisors retroclination, and mandibular autorotation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".